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ICBRA 2019 CONFERENCE ABSTRACT

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CONFERENCE ABSTRACT

2019 6th International Conference on Bioinformatics

Research and Applications (ICBRA 2019)

December 19-21, 2019

Seoul National University, Seoul, South Korea

Organized by

Supported by

Published and Indexed by

http://www.icbra.org/



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Conference Venue

Building 25-1, College of Natural Sciences, Seoul National University

Addr.: Building. 25-1 College of Natural Science, Seoul National University 1 Kwanak-ro

Kwanak-gu Seoul, 08826 South Korea

How to Get Here?

Way #1 From the Airport

(1). Incheon International Airport

• Take the ―#6003 Airport limousine bus‖ at Incheon International Airport

Get off the limousine at the main gate of Seoul National University

• Take the ―#6017 Airport limousine bus‖ at Incheon International Airport

Get off the limousine at Hoam Faculty House.

(2). Gimpo International Airport

• Take the ―#6003 Airport limousine bus‖ or ―#651 blue bus‖at Gimpo International Airport

Get off the bus at the main gate of Seoul National University

• Take the subway from Gimpo International Airport. on the No.5 line

Transfer to the No.2 line at Yeongdeungpo-gu Office Station.

Get off at either Seoul National University Station or Nakseongdae Station

Way #2 From Seoul or Yeongdeungpo Station

(1). Seoul Station

• (Take the Bus) Take the ―#501, #750A, or #750B blue bus‖ at Seoul Station

Get off at the main gate of Seoul National University

• Take the No.4 line towards Sadang

Transfer to the No.2 line at Sadang Station.

Get off at the main gate of Seoul National University Station.

(2). Yeongdeungpo Station

• Get on subway Line No.1

Transfer to the No.2 line at Sindorim Station

Get off at the Seoul National University Station.

Way #3 From Express Bus Terminal

(1). Seoul Express Bus Terminal or Cental City Terminal

• Get on ―#8541 green bus or #643 blue bus‖


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Transfer to the ―#5528 green bus‖ at Sadang 1-dong Gwanak-market Station

Get off at the main gate of Seoul National University

• Get on the No.3 subway line at Express Bus Terminal Station

Transfer to the No.2 line at Seoul National University of Education Station.

Exit 3 at Seoul National University Station

Use shuttle bus, city bus, or taxi.

(2). Dong-Seoul Bus Terminal

• Use the No.2 subway line at Gangbyeon Station

Get off at Seoul National University Station

Use shuttle bus, city bus, or taxi.


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Map


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Table of Contents Introduction 10

Conference Committee 11

Program at-a-Glance 13

Presentation Instruction 17

Keynote Speaker Introduction 18

Invited Speaker Introduction 25

Oral Session on December 19, 2019

Session 1: Medical Informatics

K0019: Massive Metagenomic Data Analysis using Microbiota and Machine

Learning

Tae-Hyuk Ahn

34

K0023: Evaluating Model-free Directional Dependency Methods on Single-cell

RNA Sequencing Data with Severe Dropout

Eliška Dvorakova, Sajal Kumar, Jiri Klema, Filip Železny, Karel Drbal and

Mingzhou Song

34

K0029: Study of Characterization of Promiscuous Binding Sites in Protein-small

Molecule Complexes

Yoichi Murakami

35

K5003: Protein Tertiary Structure Modeling Driven by Deep Learning and Contact

Distance Prediction in CASP13

Jianlin Cheng

35

K0005: Identifying the Best Metrics to Find the Best Quality Clusters of Genes from

Gene Expression Data

Raihanoor Reza Rayon, Joydhriti Choudhury, Md. Tawhidul Islam, Tanzima

Rahman Roshni, Faisal Bin Ashraf, Rasif Ajwad and Md Abdul Mottalib

36

Oral Session on December 20, 2019

Session 2: Computational Engineering and Biochemistry

K2009: Ammount and Differentiation of Cihateup Ducks Leukocytes That Fed

Supplemented with Mangosteen Peel Extract Microcapsules

Andri Kusmayadi

37


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K2008: Eco-physiological and Cytological Responses in Medicinal Species

Onopordum Alexandrinum and Alhagi Graecorum after Seed Exposure to Static

Magnetic Field

Migahid M M, El-Bakatoshi R F, Megahed S M, Amin A W and El-Sadek L M

37

K2016: Biochemical and Microbial Change in Food Fermentation ‗Ubi Karet Busuk‘

Sumba, East Nusa Tenggara, Indonesia

Periskila Dina Kali Kulla and Endah Retnaningrum

38

K0013: Computer Administered Banana Flour Processing System

Gamaliel Eve R. Minggong, Arjay D. Pabalinas, Hadassah Alysson F. Tesoro,

Randy E. Angelia and Hanna Leah P. Angelia

38

K0017: Metastatic State of Colorectal Cancer can be Accurately Predicted with

Methylome

Somayah Albaradei, Maha Thafar, Christophe Van Neste, Magbubah Essac and,

Vladimir B. Bajic

39

K4012: QCKer: An x86-AVX/AVX2 Implementation of Q-gram Counting Filter for

DNA Sequence Alignment

Joven L. Pernez Jr., Roger Luis Uy, Kaizen Vinz A. Borja and Jan Carlo G.

Maghirang

39

Session 3: Statistical Genetics

K1020: Molecular Classification of Transcriptome Expression in Serous Ovarian

Cancer using Unsupervised Clustering

Jisun Lim and Taesung Park

41

K1021: Hierarchical Component Models of Pathway Analysis for RNA Sequencing

Data

Lydia Mok, Sungyoung Lee and Taesung Park

41

K1022: Hierarchical Structural Component Model with 3-layers for

SNP-gene-pathway Analysis

Nan Jiang, Sungyoung Lee, Heungsun Hwang and Taesung Park

42

K1023: Predicting Individual Risk of Malignancy in the Patients with Intraductal

Papillary Mucinous Neoplasms of the Pancreas using Automated Machine Learning

Chanhee Lee, Hae Seung Kang, Jin-Young Jang and Taesung Park

43

K1024: The Predictive Model using Extracellular Vesicles (EVs) Microbiome

Successfully Predict Matched Pancreatic Ductal Adenocarcinoma (PDAC) and

Non-cancerous Sample

Kyulhee Han, Nayeon Kang, Jae Ri Kim, Jin-Young Jang and Taesung Park

43

K2015: A Visible Neural Network to Guide Precision Medicine

Kuenzi BM, Park J, Fong S, Ma J, Kreisberg JF and Ideker T 44

Session 4: Biomedical Engineering


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K4013: An EEG-based Depression Detection Method using Machine Learning

Model

Ran Bai, Yu Guo, Xianwu Tan, Lei Feng and Haiyong Xie

45

K4020: Identification of Raw EEG Signal for Prosthetic Hand Application

Azizi Miskon, Ayu Kusuma Sari Djonhari, Satria Mohd Haziq Azhar, Suresh A/L

Thanakodi and Siti Nooraya Mohd Tawil

45

K4024: Spatio-temporal Pattern Analysis for EEG Classification in Rapid Serial

Visual Presentation Task

Bowen Li, Zhiwen Liu, Xiaorong Gao and Yanfei Lin

46

K0011: Development of Arduino Microcontroller-based Safety Monitoring Prototype

in the Hard Hat

Robert D. Arcayena Jr, Alessis D. Ballarta, Kendall N.Claros and Rodrigo S.

Pangantihon Jr.

46

K4009: Improvement of the BT-Heartomotive Device for Avert Car Accident using

MYBradyTachyHeart Mobile Application

Mohd Azrul Hisham Mohd Adib, Muhammad Irfan Abdul Jalal and Nur Hazreen

Mohd Hasni

47

K1004: Contributions of Novel Nanomaterials to Pharmaceutical Analysis

Yixin Zhang 47

Session 5: Bioinformatics

K4014: Automated SNOMED CT Mapping of Clinical Discharge Summary Data for

Cardiology Queries in Clinical Facilities

Abdul Aziz Latip, Ma. Stella Tabora Domingo, 'Ismat Mohd Sulaiman and

Tengku Nurulhuda Tengku Abd Rahim

49

K4008: Acceptability of Virtual Reality among Older People: Ordinal Logistic

Regression Study from Taiwan

Diana Barsasella, Shankari Priya Chakkaravarthi, Hee-Jung Chung, Mina Hur,

Shabbir Syed Abdul, Shwetambara Malwade, Chia-Chi Chang, Megan F. Liu and

Yu-Chuan Li

49

K1010: Identification of Key Genes Associated with Kidney Cancer Through

Pan-cancer Bioinformatics Analysis

Nur Ain Rodzi and Suresh Kumar

50

K0008: Visualization of Differential Arm-specific miRNA Expression with TCGA

Dataset

Chao-Yu Pan and Wen-Chang Lin

50

K0030: The Method of Organizing a Service-oriented User Interface for Multi-agent

Information and Control Systems

Iakov S. Korovin, Donat Ya. Ivanov and Sergei A. Semenistyi

51

K0031: Implementation of Fingerprint Recognition using Convolutional Neural

Network and RFID Authentication Protocol on Attendance Machine

Maredi Aritonang, Irwan Doni Hutahaean, Hasudungan Sipayung and Indra

51


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Hartarto Tambunan

Session 6: Image Analysis

K0003: Identification and Classification of Export Quality Carabao Mangoes

Johannie Ave P. Ardepolla, Mike Jhon Reymar Cortez, Abigail L. Escorpion,

Jetron J. Adtoo and Kimberly M. Nepa

53

K0009: A Supervised Learning Approach on Rice Variety Classification using

Convolutional Neural Networks

Louie John L. Castillo, Juvy Amor M. Galindo and Jamie Eduardo C. Rosal

53

K0010: De-husked Coconut Quality Evaluation using Image Processing and

Machine Learning Techniques

Tito C. Lim Jr., Jaedy O. Torregosa, Aubrey Rose A. Pescadero and Rodrigo S.

Pangantihon Jr.

54

K0018: Data Mining of Daily Pig Behaviors using Wireless IC Tag based

Monitoring System in Pig Farms

Geunho Lee, Atsushi Ishimoto, Shinsuke H. Sakamoto and Seiji Ieiri

54

K0002: Supervised Machine Learning Approach for Pork Meat Freshness

Identification

Christell Faith D. Lumogdang, Christell Faith D. Lumogdang, Stephone Jone S.

Loyola, Randy E. Angelia and Hanna Leah P. Angelia

55

K0004: Automated Vermiculture Monitoring and Compost Segregating System using

Microcontrollers

Menkent S. Barcelon, Alvin A. Orilla, Jessabelle A. Mahilum and Jetron J.

Adtoon

55

Poster Session on December 20, 2019

K4010: Discrimination Colonies of Staphylococcus Aureus and Salmonella Enterica

by using Machine Learning

Manao Bunkum and Sarinporn Visitsattapongse

57

K5002: The Noninvasive Blood Glucose Monitoring by Means of Near Infrared

Sensors

Jindapa Nampeng, Yanisa Samona, Chuchart Pintavirooj, Baorong Ni and

Sarinporn Visitsattapongse

57

K4018: In Vivo Performance and Biocompatibility of an Intelligent Artificial Anal

Sphincter System

Ding Han, Guo-Zheng Yan and Kai Zhao

58

K4022: Optimization of the Treatment of Chronic Eczema in the Elderly

Zhumash Nurmukhambetov, Torgyn Ibrayeva, Alibek Nurmukhambetov and

Yerlan Bazarbekov

58

K4025: The Efficacy and Safety of Long-term Aspirin Use for Cancer Primary

Prevention: An Updated Systematic Review and Subgroup Meta-analysis of 59


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Randomized Controlled Trials

Qibiao Wu, Xiaojun Yao, Hongwei Chen and Elaine Lai-Han Leung

K1005: Rational Design of NOT-gate in Tri-node Enzyme Regulatory Networks

Xiao Wang and Xudong Lv 60

K2007: Genetic Mutations Associated with Diffuse Large B-cell Lymphoma

Jinghan Qiu 60

K4006: Comparison of Two Different Kernel Functions of Support Vector

Regression for Tracking Tumor Motion: Radial Basis Function and Linear Function

Jie Zhang, Xue Bai and Guoping Shan

60

K4007: The Accuracy Heart Dosimetric Study of Left-breast Cancer Radio-therapy

using Deformable Image Registration

Xue Bai, Shengye Wang, Binbing Wang and Jie Zhang

61

K4016: Druggability of Intrinsically Disordered Proteins and Their Virtual Screening

Strategy

Yutong Wan

62

K4019: Multiple Absorption Spectra Modeling Method for Improving Model

Stability in Spectral Analysis

Yongshun Luo, Gang Li and Ling Lin

62

Note 63


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Introduction

Welcome to 2019 6th International Conference on Bioinformatics Research and Applications (ICBRA 2019) which is organized by Biology and Bioinformatics Society (BBS) under Hong Kong Chemical, Biological & Environmental Engineering Society (CBEES), supported by Enterprise promoting world leading major departments and Information (ISSN: 2078-2489). The objective of ICBRA 2019 is to provide a platform for researchers, engineers, academicians as well as industrial professionals from all over the world to present their research results and development activities in Bioinformatics Research and Applications.

Papers will be published in the following conference proceedings:

ACM Conference Proceedings (ISBN: 978-1-4503-7218-3): archived in ACM Digital

Library, indexed by EI Compendex and SCOPUS, and submitted to be reviewed by Thomson

Reuters Conference Proceedings Citation Index (ISI Web of Science).

Some excellent papers will be recommended for reviewing of publication in one of following

journals:

Information (ISSN: 2078-2489) as a special issue, which can be indexed by Scopus

(Elsevier), EI Compendex, Emerging Sources Citation Index (ESCI-Web of Science), etc.

Genomics and Informatics (GNI, eISSN: 2234-0742) as a special issue, which can be

indexed by PubMed, PubMed Central, Scopus, Google Scholar, etc.

Conference website and email: http://www.icbra.org/; [email protected]



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Conference Committee

Conference Chairs Prof. Weizhong Li, Sun Yat-sen University, China

Prof. Taesung Park, Seoul National University, South Korea

Prof. Seungyoon Nam, Gachon University, South Korea

Assoc. Prof. Sungho Won, Seoul National University, South Korea

Program Chairs Prof. Qiang Fang, Shantou University, China

Prof. Taejin Ahn, Handong Global University, South Korea

Dr. Zhou Jianhong, Xihua University, China

Technical Committee Prof. Max Garzon, University of Memphis, USA

Prof. Kui Zhang, Michigan Technological University, USA

Prof. Yusen Zhang, Shandong University, China

Prof. Shihua Zhang, Chinese Academy of Sciences, China

Assoc. Prof. Adam G. Polak, Wrocław University of Science and Technology, Poland

Assoc. Prof. Hongyan Xu, Augusta University, USA

Assoc. Prof. Haijun Gong, Saint Louis University, USA

Assoc. Prof. Zhifu Sun, Mayo Clinic (Rochester, MN), USA

Assoc. Prof. Wen-Chang Lin, Yang-Ming University, Taiwan

Assist. Prof. Giuditta Franco, Verona University, Italy

Assist. Prof. Wooyoung Kim, University of Washington, USA

Assist. Prof. Xuan Guo, University of North Texas, USA

Dr. Asmita Sautreau, University of Portsmouth, UK

Dr. Joel P. Arrais, University of Coimbra, Portugal

Dr. Tony Smith, University of Waikato, New Zealand

Dr. Marissa Gray, Stevens Institute of Technology, USA

Dr. Wen Zhang, Icahn School of Medicine at Mount Sinai, USA

Dr. Yangyang Hao, Veracyte Inc., USA

Prof. Juan M Corchado, University of Salamanca, Spain

Dr. Jianghan Qu, Veracyte Inc., USA

Assist. Prof. Monwadee Wonglapsuwan, Prince of Songkla University, Thailand

Assoc. Prof. Yi Guo, Fudan University, China

Assoc. Prof. Yunping Zheng, South China University Of Technology, China

Dr. Balamurugan Shanmugam, Head-Reseach and Development, QUANTS - IS & CS, India

Dr. Phan Duy Hung, FPT University, Vietnam

Assoc. Prof. Jiang Gui, Dartmouth College, USA

Dr. Le Nguyen Quoc Khanh, Nanyang Technological University, Singapore

Joe Song, New Mexico State University, USA

Assist. Prof. Lijun Cheng, Ohio State University, USA


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Assoc. Prof. Dukka KC, North Carolina A&T State University, USA

Dr. Richard Edwards, University of New South Wales, Australia

Assoc. Prof. Ng To Yee Vincent, Hong Kong Polytechnic University, Hong Kong

Prof. Dongbo Bu, Chinese Academy of Sciences, China

Dr. Ka-Chun Wong, City University of Hong Kong, Hong Kong

Prof. M. Sohel Rahman, Bangladesh University of Engineering & Technology (BUET),

Bangladesh

Assoc. Prof. Gan G Redhi, Durban University of Technology, South Africa

Assoc. Prof. Shuai Cheng Li, City University of Hong Kong, Hong Kong

Dr. Chen Li, Monash University, Australia

Dr. Tae-Hyuk Ahn, Saint Louis University, USA

Assist. Prof. Mutwil Marek, Nanyang Technological University, Singapore

Prof. Bechan Sharma, University of Allahabad, India

Dr. Yasser EL-Manzalawy, Penn State University, USA


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Program at-a-Glance

December 18,

2019

(Wednesday)

Time Arrival Registration & Practice Room

(Building 26, Room102)

09:00-17:00

Short Course

Prof. Michael Greenacre, Universitat Pompeu Fabra,

Spain

Topic: ―Compositional Data Analysis in Practice I‖

December 19,

2019

(Thursday)

09:00-12:00

Short Course


Spain

Topic: ―Compositional Data Analysis in Practice II‖

December

19, 2019

(Thursday)

10:00-17:00 Arrival Registration (Lobby of Building 25-1, 1F)

Afternoon Conference (Building 25-1, International Meeting Room)

13:30-13:40

Opening Remarks

Prof. Taesung Park, Seoul National University,

South Korea

13:40-14:20

Keynote Speech I

Prof. Sun Kim, Seoul National University, South Korea

Topic: ―Measuring Intra-Tumor Heterogeneity from Bulk

Cell Sequencing‖

14:20-15:00

Keynote Speech II

Prof. Hans-Uwe Dahms

Kaohsiung Medical University, Taiwan

Topic: ―Evaluation of In silico Toxicity Predictions‖


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December

19, 2019

(Thursday)

15:00-15:30 Coffee Break & Group Photo

Time

International

Meeting Room

(Building 25-1)

Room 105

(Building 25)

15:30-15:55

Invited Speech I

Prof. Chuhsing Kate

Hsiao, National Taiwan

University, Taiwan

Topic: ―Network Analysis

for Prioritizing Regulation

Association of Hub Gene

Nodes‖

Invited Speech V

Dr. Seungyoon Nam,

Gachon University,

South Korea

Topic: ―Systems Biology in

Early Drug Discovery‖

15:55-16:20

Invited Speech II

Prof. Tzu-Pin Lu, National

Taiwan University, Taiwan

Topic: ―A Novel

Algorithm to Identify

Regulating ceRNAs using

the Integration of miRNA

and Gene Expression

Profiles‖

Session 1

Topic: ―Medical

Informatics‖

5 presentations

16:20-16:45

Invited Speech III

Dr. Minsun Song,

Sookmyung Women's

University, South Korea

Topic: ―Goodness of Fit

Test at Extreme of Disease

Risk Distribution‖

16:45-17:10

Invited Speech IV

Dr. Wonil Chung, Soongsil


Topic: ―Efficient Penalized

Regression Approaches

Improve Polygenic

Prediction in Biobank Data


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December

20, 2019

(Friday)

Arrival Registration (Lobby of Building 25-1, 1F)

Morning Conference (Building 25-1, International Meeting Room)

09:30-09:40

Opening Remarks


South Korea

09:40-10:20

Keynote Speech III

Prof. Chanchal K. Mitra, University of Hyderabad, India

Topic: ―Kinetic Modeling of Sodium Glucose

Co-transport‖

10:20-10:50 Coffee Break & Group Photo

10:50-11:30

Keynote Speech IV


Spain

Topic: ―The Analysis of High-Dimensional Microbiome

Data: It's A Question of Coherence!‖

11:30-12:10

Keynote Speech V


South Korea

Topic: ―Hierarchical Component Analysis for

Microbiome Data Using Taxonomy Information‖

12:10-13:30 Lunch (Restaurant)

Afternoon Conference

Time

International

Meeting Room

(Building 25-1)

Room 105

(Building 25)

13:30-14:00

Invited Speech VI

Dr. Sungho Won,

Seoul National University,

South Korea

Topic: ―Phylogenetic

Tree-based Microbiome

Association Test‖

Session 2

Topic: ―Computational

Engineering and

Biochemistry‖

6 presentations


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December

20, 2019

(Friday)

14:00-14:30

Invited Speech VII

Prof. Yujin Chung,

Kyonggi University,

South Korea

Topic: ―Inference of

Isolation-with-migration

Models from Genomic

Data

Session 2

Topic: ―Computational

Engineering and

Biochemistry‖

6 presentations

(-continued)

14:30-15:00

Invited Speech VIII

Dr. Iksoo Huh, College of

Nursing and Research

Institute of Nursing

Science, Seoul National


Topic: ―Enhanced

permutation approach via

pruning‖

15:00-15:15 Coffee Break and Poster Session

Time

International

Meeting Room

(Building 25-1)

Room 105

(Building 25)

15:15-16:45

Session 3

Topic: ―Statistical

Genetics‖

6 presentations

Session 4

Topic: ―Biomedical

Engineering‖

6 presentations

16:45-17:00 Coffee Break and Poster Session

17:00-18:30

Session 5

Topic: ―Bioinformatics‖

6 presentations

Session 6

Topic: ―Image Analysis‖

6 presentations

18:30-20:00 Dinner (Restaurant)

December

21, 2019

(Saturday)

10:00-11:30

Academic Visit

Graduate School of Public Health,

Medical science & Bioinformatics Lab.

Tips: Please arrive at the Conference Room 10 minutes before the session begins to upload PPT into

the laptop; submit the poster to the staff when signing in.


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Presentation Instruction

Instruction for Oral Presentation

Devices Provided by the Conference Organizer:

Laptop Computer (MS Windows Operating System with MS PowerPoint and Adobe Acrobat

Reader); Digital Projectors and Screen; Laser Stick

Materials Provided by the Presenters:

PowerPoint or PDF Files (Files should be copied to the Conference laptop at the beginning of

each Session.)

Duration of each Presentation (Tentatively):

Keynote Speech: about 35 Minutes of Presentation and 5 Minutes of Question and Answer

Invited Speech: about 20/25 Minutes of Presentation and 5 Minutes of Question and Answer

Oral Presentation: about 12 Minutes of Presentation and 3 Minutes of Question and Answer

Instruction for Poster Presentation

Materials Provided by the Conference Organizer:

The place to put poster

Materials Provided by the Presenters:

Home-Made Posters: Submit the poster to the staff when signing in; Poster Size: A1

(841*594mm); Load Capacity: Holds up to 0.5 kg

Best Presentation Award One Best Oral or Poster Presentation will be selected from each session, and the Certificate

for Best Presentation will be awarded at the end of the session on Dec. 19 and Dec. 20, 2019.

Dress Code Please wear formal clothes or national representative of clothing.

Disclaimer Along with your registration, you will receive your name badge, which must be worn when

attending all conference sessions and activities. Participants without a badge will not be

allowed to enter the conference venue. Please do not lend your name badge to the persons

who are not involved in the conference and do not bring the irrelevant persons into the

conference venue.

The conference organizers cannot accept liability for personal injuries, or for loss or damage

of property belonging to conference participants, either during, or as a result of the conference.

Please check the validity of your own insurance.


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Keynote Speaker Introduction

Keynote Speaker I

Prof. Sun Kim

Seoul National University, South Korea

Sun Kim is Professor in the School of Computer Science and Engineering, Director of

Bioinformatics Institute, and an affiliated faculty for the Interdisciplinary Program in

Bioinformatics at Seoul National University. Before joining SNU, he was Chair of Faculty

Division C; Director of Center for Bioinformatics Research, an Associate Professor in School

of Informatics and Computing; and an Adjunct Associate Professor of Cellular and Integrative

Physiology, Medical Sciences Program at Indiana University (IU) Bloomington. Prior to

joining IU in 2001, he worked at DuPont Central Research from 1998 to 2001, and at the

University of Illinois at Urbana-Champaign from 1997 to 1998. Sun Kim received B.S and

M.S and Ph.D in Computer Science from Seoul National University, KAIST and the

University of Iowa, respectively.

Topic: “Measuring Intra-Tumor Heterogeneity from Bulk Cell Sequencing”

Abstract—Intratumor heterogeneity (ITH) represents various phenotypic diversity among

subclones that constitute a cancer tissue. ITH is now considered as an important clinical factor

related to the aggressiveness, drug resistance, recurrence, and metastasis of cancer. Since

cancer is a disease of the genome, the ITH level and cancer subclonal structure are inferred

based on the genomic profile (e.g. somatic mutations and copy number variations). However,

recent studies have suggested that the ITH can be identified at multi-omics level. Recently,

our group developed ITH inference methods for methylome, transcriptome, and spliceome

bulk-tumor sequencing data. The first method (Scientific Report 2016) that we developed was

a transcriptomic ITH (tITH) model that measured entropy of biological network states. We

developed another information theoretic method for measuring spliceomic ITH (sITH) in

cancer cells, SpliceHetero (PLoS ONE 2019). Splicing patterns in cancer are very

complicated, including wide spread retention of intron sequences in transcripts. The last one,

PRISM (ISMB/Bioinformatics 2019), is a tool for inferring the composition of epigenetically

distinct subclones of a tumor solely from methylation patterns obtained by reduced

representation bisulfite sequencing.


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Keynote Speaker II

Prof. Hans-Uwe Dahms

Kaohsiung Medical University, Taiwan

Dr. Hans-Uwe Dahms is a professor at Kaohsiung Medical University. He is interested in

stress responses in general and within aquatic systems in particular. He, his colleagues and

students integratively study pollution and the toxicology of stressors from physical, chemical,

and biological sources. He is equally interested in climate change, the spread of diseases,

antibiotic-resistance, food and drink safety from water sources, and integra-tive approaches in

environmental and public health monitoring, risk assessment and management. He advised

more than 25 Ph.D. students in their research and published more than 275 papers in scientific

journals. He served as a reviewer for more than 70 SCI journals, as editorial board member of

12 reputed scientific journals, academic editor of PLosONE, and as editor in chief of

FRONTIERS in Marine Pollution.

Topic: “Evaluation of in silico Toxicity Predictions”

Abstract—Chemoinformatics represents a search for chemical information resources where

data are typically transformed into information and this into technologies that allow to make

decisions better and faster. Such in silico approaches refer to computer applications or

computer simulations. In silico approaches in pollution studies can best be understood as

chemoinformatics using informational techniques applied to a range of problems in the field

of chemistry related to toxicology and the effects of pollutants. To provide an example for the

evaluation of in silico approaches, we will introduce to food safety issues related to food

preservatives, plasticizers, and artificial sweeteners. For such assessments SMILES of the

above food additives will be taken from the PubChem database. By using MarvinSketch all

chemicals presented here are based on structural data retrieved from PubChem. In silico

predictive models generally provide fast and economic screening tools for compound

properties. They allow a high throughput and a constant optimization. They are less expensive,

less time consuming, have a high reproducibility, and reduce experimental efforts.

Computational approaches can also prioritize chemicals for their toxicological evaluation in

order to reduce the amount of costly in vitroand ethically problematic in vivo toxicological

screenings, and provide early alerts for newly developed substances. Limitations include that

ADME aspects (absorption, distribution, metabolism, and excretion – which are basic

pharmacokinetic descriptors) are not taken into account. There can be a lack of quality and


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transparency of the training set of experimental data. The programs, descriptors, and

applicabilities are sometimes not clear. In addition are carcinogenicity predictions not possible

based on non-genotoxic compounds.


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Keynote Speaker III

Prof. Chanchal K. Mitra

University of Hyderabad, India

Chanchal Kumar Mitra (born November 02, 1950; W Bengal, India) is currently

(2015-2017) an UGC (University Grants Commission, Government of India) Emeritus

Professor at the Department of Biochemistry, University of Hyderabad. He obtained his B.Sc.

(Bachelor of Science) degree from the Presidency College (University of Calcutta) with

Chemistry as the main subject (1969) and his M.Sc. (Master of Science) degree from the same

university in 1971 in Pure Chemistry. He did his doctoral work at the Tata Institute of

Fundamental Research, Bombay (now Mumbai) on computational studies on the

conformations of several aza-nucleosides and received his Ph.D. degree from the University

of Bombay in 1977. He did post doctoral work at the University at Albany (New York, USA)

and University of Lund (Sweden). He joined the University of Hyderabad in 1985 and retired

in 2015. His current research interests are (I) biosensors and (II) modeling of metabolic

pathways. He has a number of publications in the relevant areas which can be found from

https://www.researchgate.net/profile/Chanchal_Mitra/contributions.

Topic: “Kinetic Modeling of Sodium Glucose Co-transport”

Abstract— A simulation of the kinetics of the sodium-glucose transporters has been reported

using a model widely used in literature. However, the various kinetic constants of the

transporter have been replaced by 1 (as they are not available in the literature). We have also

studied the effect of the membrane potential on glucose transport. The used model is leaky,

i.e., sodium transport can take place independently of glucose transport. Although the results

can be considered only semi-quantitative, we find that glucose transport is rather

energy-intensive, because around 15 sodium ions needed to be transported for each glucose

molecule carried inside. However, the process is powerful, in the sense that the final glucose

concentration outside can fall almost to zero


- 22 -

Keynote Speaker IV

Prof. Michael Greenacre

Universitat Pompeu Fabra, Spain

Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra in Barcelona.

His whole career has been devoted to research in multivariate analysis and he has written six

books on correspondence analysis and data visualization and co-edited four more with Prof.

Jörg Blasius (Bonn University). He has over 100 published articles in peer-reviewed journals

and books, and has given short courses in 15 countries to statisticians, biologists and social

scientists, in Europe, North and South America, Africa and Australia. For more than 30 years

he has been working in projects related to Arctic ecology, based in north Norway. And for

almost 20 years he has become interested in compositional data analysis, collaborating with

biochemists, geochemists and recently with researchers in the analysis of microbiome data.

Topic: “The Analysis of High-Dimensional Microbiome Data: It's A Question of

Coherence!”

Abstract—The standard structure of a microbiome data set is: (1) high-dimensional (hundreds

or thousands of variables, in the form of operational taxonomics units, or OTUs); (2)

relatively small sample (tens or hundreds); (3) basic data are counts of OTUs in each

sampling unit; (4) many zeros (50-90% of the data set are zeros); and (5) the totals in each

sampling unit are irrelevant, it is the relative counts that are important. To try to understand

these data and extract some meaning from them, the problem might either be (a) to identify

the OTUs that are driving the overall structure, which means equivalently removing those

OTUs that can be considered random and uninteresting; (b) when there is some specific

objective such as to explain a response variable or distinguish between pre-defined groups, to

identify the OTUs that are relevant to this objective. In either case we have the challenge of

variable selection. In this talk I will describe the approach to such data known as

compositional data analysis. The basic principle of this approach is that the analytical

procedure be subcompositionally coherent, which dictates that ratios of OTUs be used rather

than the OTUs themselves. The problem with this approach is that zero values are not

permitted, so there are various strategies to cope with this situation. One way is to replace the

zeros by some small positive values, while a pragmatic solution is to use an alternative

approach for which zeros need no replacement, while deviating as little as possible from the

ideal requirement of subcompositional coherence.


- 23 -

Keynote Speaker V

Prof. Taesung Park


Prof. Taesung Park received his B.S. and M.S. degrees in Statistics from Seoul National

University (SNU), Korea in 1984 and 1986, respectively and received his Ph.D. degree in

Biostatistics from the University of Michigan in 1990. From Aug. 1991 to Aug. 1992, he

worked as a visiting scientist at the NIH, USA. From Sep. 2002 to Aug. 2003, he was a

visiting professor at the University of Pittsburgh. From Sep. 2009 to Aug. 2010, he was a

visiting professor in Department of Biostatistics at the University of Washington. From Sep.

1999 to Sep. 2001, he worked as an associate professor in Department of Statistics at SNU.

Since Oct. 2001 he worked as a professor and currently the Director of the Bioinformatics and

Biostatistics Lab. at SNU. He served as the chair of the bioinformatics Program from Apr.

2005 to Mar. 2008, and the chair of Department of Statistics of SNU from Sep. 2007 and Aug.

2009. He has served editorial board members and associate editors for the international

journals including Genetic Epidemiology, Computational Statistics and Data Analysis,

Biometrical Journal, and International journal of Data Mining and Bioinformatics. His

research areas include microarray data analysis, GWAS, gene-gene interaction analysis, and

statistical genetics.

Topic: “Hierarchical Component Analysis for Microbiome Data Using Taxonomy

Information”

Abstract—The recent advent of high-throughput sequencing technology has enabled us to

study the associations between human microbiome and diseases. The DNA sequences of

microbiome samples are clustered as operational taxonomic units (OTUs) according to their

similarity. The OTU table containing counts of OTUs present in each sample is used to

measure correlations between OTUs and disease status and find key microbes for prediction

of the disease status. Various statistical methods have been proposed for such microbiome

data analysis. However, none of these methods have used hierarchical structure of taxonomy

information that is biologically meaningful. In this paper, we propose a hierarchical structural

component model for microbiome data (HisCoM-microb) using taxonomy information as

well as OTU table data. The proposed HisCoM-microb consists of two layers: one for OTUs

grouped at the lowest taxonomy level and the other for OTUs grouped at the higher taxonomy

level. Then we calculate simultaneously coefficient estimates of OTUs of all layers inserted in

the hierarchical model. Through this analysis, we can infer the association between OTUs and


- 24 -

disease status, considering the impact of taxonomic structure on disease status. Both

simulation study and real microbiome data analysis show that our method provides a new

testing approach for microbiome data which clearly reveal the relations between each taxon

and disease status at the same time as finding the key microbiota of the disease.


- 25 -

Invited Speaker Introduction

Invited Speaker I

Prof. Chuhsing Kate Hsiao

National Taiwan University, Taiwan

Chuhsing Kate Hsiao, professor at National Taiwan University, received her PhD in

Statistics at Carnegie Mellon University and then joined the Division of Biostatistics in

Institute of Epidemiology and Preventive Medicine, College of Public Health in 1994. She

was the Associate Dean of the college and Department Head of Public Health in 2011-2013.

She served in the ICSA Board of Directors from 2014 to 2016, and was awarded Distinguish

Professor in 2015. Her research interests focus on the development of methodology for

genetic association studies, including Bayesian mixture models for GWAS, regularized

support vector regression for gene selection, Hamming distance-based clustering algorithm,

integrative analysis of multi-omics genomic variants and network/pathway analysis for

multiple genetic markers. She also enjoys inter-disciplinary collaborations such as the

national myopic survey, risk evaluation of air pollutant and temperature on coronary heart

diseases and probabilistic ensemble forecast of typhoon precipitation.

Topic: “Network Analysis for Prioritizing Regulation Association of Hub Gene Nodes”

Abstract—To identify and prioritize the influential hub genes in a gene-set or biological

pathway, most analyses rely on calculation of marginal effects or tests of statistical

significance. Such procedures may be inappropriate if dependence between gene nodes exists,

and if the hub nodes require more attention than others. The highly connected hub genes may

play a more important role for the whole network to function properly. To prioritize the hub

gene nodes, here we develop a pathway activity score incorporating the local effect of gene

nodes as well as intra-network affinity measures. This score summarizes the expression levels

in a gene-set/pathway for each sample, with weights on local and network information,

respectively. The score is next used to examine the impact of each node through a

leave-one-out evaluation. Two cancer studies, one involving RNA-Seq from breast cancer

patients with high-grade ductal carcinoma in situ and one microarray expression data from

ovarian cancer patients, and simulation analysis are used to assess the performance of the


- 26 -

procedure, and to compare with existing methods with/without consideration of correlation

and network information.


- 27 -

Invited Speaker II

Prof. Tzu-Pin Lu

National Taiwan University, Taiwan

Prof. Tzu-Pin Lu got his Ph.D. degree in the institute of Biomedical Engineering and

Bioinformatics, National Taiwan University. He served as a postdoc fellow for the YongLin

Biomedical Engineering Center, National Taiwan University and the Division of

Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug

Administration, USA. After that, he joined the institute of Epidemiology and Preventive

Medicine, National Taiwan University and currently he is an associate professor. His major

research interests include utilizing high-through genomics and genetics data to study different

diseases, such as breast cancer, lung cancer and cardiovascular diseases. In addition, he

developed several online databases, analytical systems and R packages to facilitate analyzing

genetics data.

Topic: “A Novel Algorithm to Identify Regulating ceRNAs using the Integration of miRNA

and Gene Expression Profiles”

Abstract—In recent years, researchers can examine multiple omics data in the same individual.

Among different molecules, microRNA (miRNA) is the most studied non-coding RNA.

Several studies report the regulatory effect of one miRNA to its target genes depends on its

own miRNA expression level, which is named as a competing endogeneous RNA (ceRNA)

and miRNA pair. Currently, most algorithms need to define different groups based on the

expression level of the miRNA. However, challenge arises; the expression level of a miRNA

is actually a continuous variable instead of a discrete variable. To address this issue, we

developed a novel algorithm to identify ceRNA-miRNA pairs. First, a random walk method

was used to exclude miRNA-gene pairs without any correlation. Subsequently, a circular

binary segmentation algorithm was applied to obtain the peaks of the miRNA expression

levels across different samples. A simulation study with different scenarios demonstrated that

our algorithm is efficient and accurate to identify true ceRNA-miRNA pairs. Lastly, two real

cancer datasets from The Cancer Genome Atlas (TCGA) were analyzed by our algorithm. The

results suggest that our approach not only is able to validate previous findings from other

studies but also can reveal several new ceRNA-miRNA candidates.


- 28 -

Invited Speaker III

Dr. Minsun Song

Sookmyung Women's University, South Korea

Dr. Song received a B.S. and M.S. in statistics from Seoul National University and a Ph.D. in

statistics from the University of Chicago. After that, she worked as a postdoctoral research

associate at the Lewis-Sigler Institute for Integrative Genomics at Princeton University. Dr.

Song joined the Biostatistics Branch, Division of Cancer Epidemiology and Genetics,

National Cancer Institute, National Institutes of Health as a postdoctoral fellow and later

joined Department of Mathematics and Statistics at University of Nevada Reno as an assistant

professor. Dr. Song now serves an assistant professor at Department of Statistics at

Sookmyung Women's University. Her current research is focused on development of

statistical methodologies with genetic or genomic data. Especially, her statistical research

interests include analysis of high-dimensional data in the presence of latent variables,

high-dimensional large-scale modeling, and testing for gene-gene and gene-environment

interaction.

Topic: “Goodness of Fit Test at Extreme of Disease Risk Distribution”

Abstract—Risk-prediction models need careful calibration to ensure they produce unbiased

estimates of risk for subjects in the underlying population given their risk-factor profiles. As

subjects with extreme high or low risk may be the most affected by knowledge of their risk

estimates, checking the adequacy of risk models at the extremes of risk is very important for

clinical applications. We propose a new approach to test model calibration targeted toward

extremes of disease risk distribution where standard goodness-of-fit tests may lack power due

to sparseness of data. We construct a test statistic based on model residuals summed over only

those individuals who pass high and/or low risk thresholds and then maximize the test statistic

over different risk thresholds. We derive an asymptotic distribution for the max-test statistic

based on analytic derivation of the variance-covariance function of the underlying Gaussian

process. The method is applied to a large case-control study of breast cancer to examine joint

effects of common single nucleotide polymorphisms (SNPs) discovered through recent

genome-wide association studies. The analysis clearly indicates a non-additive effect of the

SNPs on the scale of absolute risk, but an excellent fit for the linear-logistic model even at the

extremes of risks.


- 29 -

Invited Speaker IV

Dr. Wonil Chung

Soongsil University, South Korea

Wonil Chung is an assistant professor in Statistics at Soongsil University. I was a research

associate at Harvard T.H. Chan School of Public Health and received Ph.D. in Biostatistics

from the University of North Carolina at Chapel Hill and MS, BS in Statistics from Seoul

National University. As my early career, I worked for an IT company as a computer

programmer and thus have programming skill in C/C++, Java, Python, R as well as parallel

computing. During my doctoral and postdoctoral years, I have developed novel statistical

methodologies for genome-wide association studies (GWAS), quantitative trait loci (QTL),

expression QTL (eQTL) mapping and genomic risk prediction. Also, I have participated in a

variety of large-scale omics projects such as identification of shared genetic architecture and

analyses of methylation and metabolomics data.

Topic: “Efficient Penalized Regression Approaches Improve Polygenic Prediction in

Biobank Data”

Abstract—We introduce CTPR (Cross-Trait Penalized Regression), a powerful and practical

approach for multi-trait polygenic risk prediction in Biobank-scale cohorts. Specifically, we

propose a novel cross-trait penalty function with Lasso and MCP to incorporate the shared

genetic effects across multiple traits for large-sample GWAS data. Our approach extracts

information from the secondary traits that is beneficial for predicting the primary trait based

on individual-level genotypes and/or summary statistics. Our novel implementation of a

parallel computing algorithm makes it feasible to apply our method to biobank-scale GWAS

data. Next, we extend our CTPR method to multi-ethnic GWAS data by modelling

population-specific LD. The predictive performance of CTPR (Cross-eThnic Penalized

Regression) will be compared with the existing multi-ethnic prediction methods.


- 30 -

Invited Speaker V

Assist. Prof. Seungyoon Nam

Gachon University, South Korea

Seungyoon Nam received the PhD degree in bioinformatics from Seoul National University,

in 2008. From 2008 to 2010, he was a postdoctoral fellow with the Indiana University School

of Medicine, Indiana, performing computational cancer epigenomics studies. From 2010 to

2015, he worked as a senior researcher in a field of cancer bioinformatics at the Korea

Institute of Information and Science Technology, and the National Cancer Center of Korea.

Since 2015, he has been an assistant professor in the College of Medicine, Gachon University,

Korea. His research interests include systems biology, miRNA biology, Next-Generation

Sequencing (NGS) clinical tests, and druggable genome in various diseases. He has served as

a member of the program committee at the IEEE International Conference on Bioinformatics

& Biomedicine, Workshop on Data Mining from Genomic Rare Variants and Its Application

to Genome-Wide Analysis since 2014

Topic: “Systems Biology in Early Drug Discovery”

Abstract—Biologists have studied individual biological entities. It partly resulted from no

available public datatasets regarding their entities in interests. But, the situation has been

changed drastically in cancer. High-throughput sequencing datasets (i.e., ―big data‖) have

been poured in public data repositories, and the datasets are now available freely.

Accumulation of these datasets have now included experimental measurements (e.g., mRNA

levels, mutations) for exhaustively diverse biological entities (so called ―Omics‖) including

biologists‘ own entities in interests. From these high-throughput datasets, a global

understanding (equivalently, system-level understanding or systems biology) of molecular

mechanisms is now allowed in phenotype changes in interests. In this talk, systems biology

will be introduced in the field of medicine. Finally, association of systems biology and

druggable target discovery will be discussed shortly.


- 31 -

Invited Speaker VI

Sungho Won


Topic: “Phylogenetic Tree-based Microbiome Association Test”

Abstract—Motivation: Ecological patterns of the human microbiota exhibit high inter-subject

variation, with few operational taxonomic units (OTUs) shared across individuals. To

overcome these issues, non-parametric approaches, such as the Mann–Whitney U-test and

Wilcoxon rank-sum test, have often been used to identify OTUs associated with host diseases.

However, these approaches only use the ranks of observed relative abundances, leading to

information loss, and are associated with high false-negative rates. In this study, we propose a

phylogenetic tree-based microbiome association test (TMAT) to analyze the associations

between microbiome OTU abundances and disease phenotypes. Phylogenetic trees illustrate

patterns of similarity among different OTUs, and TMAT provides an efficient method for

utilizing such information for association analyses. The proposed TMAT provides test

statistics for each node, which are combined to identify mutations associated with host

diseases.

Results: Power estimates of TMAT were compared with existing methods using extensive

simulations based on real absolute abundances. Simulation studies showed that TMAT

preserves the nominal type-1 error rate, and estimates of its statistical power generally

outperformed existing methods in the considered scenarios. Furthermore, TMAT can be used

to detect phylogenetic mutations associated with host diseases, providing more in-depth

insight into bacterial pathology.

Availability: The 16S rRNA amplicon sequencing metagenomics datasets for colorectal

carcinoma and myalgic encephalomyelitis/chronic fatigue syndrome are available from the

European Nucleotide Archive (ENA) database under project accession number PRJEB6070

and PRJEB13092, respectively. TMAT was implemented in the R package. Detailed

information is available at http://healthstat.snu.ac.kr/software/tmat.


- 32 -

Invited Speaker VII

Dr. Yujin Chung

Kyonggi University, South Korea

Dr. Yujin Chung is a tenure-track assistant professor in the Department of Applied Statistics

at Kyonggi University, South Korea. Her research areas include statistical phylogenetics,

biostatistics, bioinformatics, and Bayesian analysis. Dr. Chung received a Ph. D. in Statistics

from the University of Wisconsin - Madison, USA. She was a postdoctoral fellow and a

research assistant professor at the center for computational genetics and genomics (CCGG) at

Temple University, USA. Before moving back to South Korea, she was a tenure-track

assistant professor in the Department of Statistics at Auburn University, USA.

Topic: “Inference of Isolation-with-migration Models from Genomic Data”

Abstract—Isolation-with-migration (IM) models explain the divergence of populations by the

processes of genetic drift and migrations. Due to recent sequencing and computing advances,

statistical inference has played an important role in the study of evolutionary history from

genomic data. However, typical analyses are either limited to a small amount of data or fail to

estimate complex and diverse evolutionary models. In this talk, I will present a new Bayesian

method for estimating IM models including population sizes, splitting time of two populations,

and migration rates. The new method resolves statistical limitations and overcomes major

roadblocks to analyze genome-scale data. Using importance sampling and a Markov chain

representation of genealogy, the new method scales to genomic data without mixing difficulty

in a Markov chain Monte Carlo simulation. I will demonstrate the new method with simulated

data and real DNA sequences.


- 33 -

Invited Speaker VIII

Dr. Iksoo Huh


Iksoo Huh received his Ph.D. degree in Statistics from Seoul National University, South

Korea, in 2015, and he was a postdoctoral fellow at the School of Biological Sciences,

Georgia Institute of Technology, USA.

Topic: “Enhanced permutation approach via pruning”

Abstract— Big multi-omics data for bioinformatics area consists of huge numbers of features,

but relatively small number of samples. In addition, the features from multi-omics data have

their own specific characteristics depending on whether they are from genomics, proteomics,

metabolomics, and so forth. Due to these various characteristics, standard statistical analysis

approaches based on parametric assumptions may sometimes fail to provide exact asymptotic

results. In order to resolve the issue, permutation test can be a way to exact analysis of

multi-omics data, because it is distribution-free and flexible to use. In permutation tests,

p-values are evaluated by estimating location of test statistic in an empirical null distribution

which is generated by random shuffling. However, the permutation approach can be infeasible

when number of features becomes larger, because more stringent control of the type I error for

multiple hypothesis testing is needed, and consequently much larger number of permutation is

required to reach the significant level. To address the problem, we propose a well-organized

strategy for enhanced permutation tests via multiple pruning (ENPP). ENPP prunes the

features in every permutation round if they are determined to be non-significant. In other

words, if a feature has more times that statistics from permuted data sets exceed the original

statistics than a certain number of pre-determined cutoff, it will be determined to be

non-significant, and ENPP removes the feature and iterates the process without the feature in

the next permutation round. Our simulation study showed that the ENPP method could

remove about 50% features at the first permutation round, and in the 100th permutation round,

98% of the features were removed and only 7.4% of computation time was required when

compared to original unpruned permutation approach. In addition, we applied this approach to

a real data set (Korea Association REsource: KARE) which has 327,872 SNPs to find

association with a non-normal distributed phenotype (fasting plasma glucose, FPG),

interpreted the results, and discussed feasibility and advantage of the approach.


- 34 -

Session 1

Tips: The schedule for each presentation is for reference only. In order not to miss your presentation,

we strongly suggest that you attend the whole session.

Afternoon, December 19, 2019 (Thursday)

Time: 15:55-17:10

Venue: Room 105

Topic: “Medical Informatics”

Session Chair: Prof. Hans-Uwe Dahms

K0019

Session 1

Presentation 1

(15:55-16:10)

Massive Metagenomic Data Analysis using Microbiota and Machine

Learning

Tae-Hyuk Ahn

Saint Louis University, USA

Abstract—Metagenomics is the application of modern genomic techniques

to investigate the members of a microbial community directly in their

natural environments and is widely used in many studies to survey the

communities of microbial organisms that live in diverse ecosystems. In

order to understand the metagenomic profile of one of the densest

interaction spaces for millions of people, the MetaSUB International

Consortium has collected and sequenced metagenomes from subways of

different cities across the world. To distinguish the metagenomic profiling

among different cities and also predict unknown samples precisely based on

the profiling, two different approaches are proposed using machine learning

techniques; one is a read-based taxonomy profiling of each sample and

prediction method, and the other is a reduced representation assembly-based

method. Among various machine learning techniques tested, the random

forest technique showed promising results as a suitable classifier for both

approaches with 98% research topics. We also developed a versatile R

package to analyze massive and diverse microbiome profiles of

metagenomics samples quickly and accurately using machine learning.

Based on the interactive web-supporting library, R-Shiny, the proposed

software provides user-friendly functions and options for data

preprocessing, model development, model validation, and independent

sample prediction.

K0023

Session 1

Presentation 2

Evaluating Model-free Directional Dependency Methods on Single-cell

RNA Sequencing Data with Severe Dropout

Eliška Dvoˇrakova, Sajal Kumar, Jiˇri Klema, Filip Železny, Karel Drbal

and Mingzhou Song

New Mexico State University, USA


- 35 -

(16:10-16:25)

Abstract—As severe dropout in single-cell RNA sequencing (scRNAseq)

degrades data quality, current methods for network inference face increased

uncertainty from such data. To examine how dropout influences directional

dependency inference from scRNA-seq data, we thus studied four methods

based on discrete data that are model-free without parametric model

assumptions. They include two established methods: conditional entropy

and Kruskal-Wallis test, and two recent methods: causal inference by

stochastic complexity and function index. We also included three

non-directional methods for a contrast. On simulated data, function index

performed most favorably at varying dropout rates, sample sizes, and

discrete levels. On an scRNA-seq dataset from developing mouse cerebella,

function index and Kruskal-Wallis test performed favorably over other

methods in detecting expression of developmental genes as a function of

time. Overall among the four methods, function index is most resistant to

dropout for both directional and dependency inference. The next best

choice, Kruskal-Wallis test, carries a directional bias towards a uniformly

distributed variable. We conclude that a method robust to marginal

distributions with a sufficiently large sample size can reap benefits of

single-cell over bulk RNA sequencing in understanding molecular

mechanisms at the cellular resolution.

K0029

Session 1

Presentation 3

(16:25-16:40)

Study of Characterization of Promiscuous Binding Sites in Protein-small

Molecule Complexes

Yoichi Murakami

Tokyo University of Information Sciences, Japan

Abstract—An exhaustive comparison of different proteins has provided new

insights into the characteristics of many proteins, leading to understanding

their molecular and biological functions. Although many research works

have so far characterized binding sites (BS) in proteins, only a few

research-works about promiscuous BS which can accommodate different

ligands or compounds have been presented and the knowledge is still

limited. Thus, in this study, the promiscuous BS in protein-small molecule

complexes from the Protein Data Bank (PDB) were exhaustively compared

with the non-promiscuous BS to reveal physicochemical and structural

properties of their BS. As a result, aliphatic, aromatic, and sulfur-containing

amino acids (AA) were more likely to appear in promiscuous BS, indicating

that they tend to be more hydrophobic than non-promiscuous BS.

Furthermore, the number of AA and the accessible surface area of

promiscuous BS tended to be larger than those of non-promiscuous BS. In

addition, the significant difference of α-helix between promiscuous BS and

non-promiscuous BS was observed.

K5003

Session 1

Protein Tertiary Structure Modeling Driven by Deep Learning and Contact

Distance Prediction in CASP13

Jianlin Cheng

University of Missouri, USA


- 36 -

Presentation 4

(16:40-16:55)

Abstract—Predicting residue‐residue distance relationships (e.g., contacts)

has become the key direction to advance protein structure prediction since

2014 CASP11 experiment, while deep learning has revolutionized the

technology for contact and distance distribution prediction since its debut in

2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced

our MULTICOM protein structure prediction system with three major

components: contact distance prediction based on deep convolutional neural

networks, distance‐driven template‐free (ab initio) modeling, and protein

model ranking empowered by deep learning and contact prediction. Our

experiment demonstrates that contact distance prediction and deep learning

methods are the key reasons that MULTICOM was ranked 3rd out of all 98

predictors in both template‐free and template‐based structure modeling in

CASP13. The success of MULTICOM system clearly shows that protein

contact distance prediction and model selection driven by deep learning holds

the key of solving protein structure prediction problem. However, there are

still challenges in accurately predicting protein contact distance when there

are few homologous sequences, folding proteins from noisy contact

distances, and ranking models of hard targets.

K0005

Session 1

Presentation 5

(16:55-17:10)

Identifying the Best Metrics to Find the Best Quality Clusters of Genes from

Gene Expression Data

Raihanoor Reza Rayon, Joydhriti Choudhury, Md. Tawhidul Islam,

Tanzima Rahman Roshni, Faisal Bin Ashraf, Rasif Ajwad and Md Abdul

Mottalib

Brac University, Bangladesh

Abstract—With the recent advancement of computing technique and data

availability in the field of computational biology, it has been a great

opportunity for the scientists to find the evolutionary relation among the

living beings in terms of their genotypic and phenotypic attributes.

Microarray, one of the efficient ways to store the expression level of genes

in the living being, can be used to create groups from a set of genes based on

their phenotypic information. This information plays an important role in

pathway analysis, disease prediction, target identification in drug design and

many other important functionalities and applications in biology. However,

it has become a great challenge over time to select a particular distance

metric to calculate the similarity between the genes. In this work, we have

studied 16 possible combinations of metrics to find the groups of similar

genes in terms of their expression level by building their phylogenetic

relation and keeping the most related genes together. Moreover, we have

validated our findings by evaluating the output of the same trials on

different data sets. We have found that, for grouping the similar genes

together by building a Phylogenetic Tree, Maximum Distance Metric and

Average Linkage tends to give the best quality.


- 37 -

Session 2



Afternoon, December 20, 2019 (Friday)

Time: 13:30-15:00

Venue: Room 105

Topic: “Computational Engineering and Biochemistry”

Session Chair: Prof. Chanchal K. Mitra

K2009

Session 2

Presentation 1

(13:30-13:45)

Ammount and Differentiation of Cihateup Ducks Leukocytes That Fed

Supplemented with Mangosteen Peel Extract Microcapsules

Andri Kusmayadi

Universitas Padjadjaran, Indonesia

Abstract—This study aimed to examine the effects of the level of

mangosteen peel extract microcapsules (MPEM) on the amount and

differentiation of Cihateup duck leukocytes. The treatments were tested

consisting of 0% MPEM (T1), 0.5% MPEM (T2), 1.0% MPEM (T3), 1.5%

MPEM (T4), 2.0% MPEM (T5), 2.5% MPEM (T6) and 50 ppm bacitracin

as positive control (T7). The research data were tested using ANOVA

method and continued by Duncan test if there was a significant difference.

In the leukocytes amount test, the MPEM treatment did not have a

significant effect (P>0.05). Meanwhile, MPEM treatment had a significant

effect (P<0.05) on all leukocytes differentiation parameters (heterophils,

eosinophils, basophils, lymphocytes, and monocytes). This proved that the

MPEM treatment had the ability to improve the leukocytes differentiation of

Cihateup ducks. In this study, the level of 2.0% MPEM had better results

than the other treatments.

K2008

Session 2

Presentation 2

(13:45-14:00)

Eco-physiological and Cytological Responses in Medicinal Species

Onopordum Alexandrinum and Alhagi Graecorum after Seed Exposure to

Static Magnetic Field

Migahid M M, El-Bakatoshi R F, Megahed S M, Amin A W and El-Sadek L

M

Alexandria University, Egypt

Abstract—Two medicinal plants Onopordum alexandrinum Boiss. (Family

Asteraceae) and Alhagi graecorum Medic. (Family Fabaceae) were selected

to determine the effect of different intensities and exposure times of static

magnetic field on eco-physiological and cytological levels. The results

indicated that shoot and root lengths of O. alexandrinum decreased


- 38 -

significantly in contrast to A. graecorum, where the length increased

gradually with magnetic field compared to control. A significant reduction

of chlorophyll a was recorded in A. graecorum in response to treatments;

however significant variation in chlorophyll content of O. alexandrinum was

recorded. The total phenolic content of O. alexandrinum decreased

significantly compared to control; while in the case of A. graecorum high

significant accumulation was recorded. The total flavenoid content in the

two species exhibited a significant reduction due to changing exposure time

of magnetic field. Significant increases in the mitotic index of both species

root meristems were recorded under seed treatments. The highest intensity

induced significant increases in chromosomal aberrations of O.

alexandrinum but different intensity showed highly significant increase in A.

graecorum. Remarkable trends were recorded toward higher tolerance in A.

graecorum compared to O. alexandrinum under magnetic effect. This opens

an unusual perspective on plant adaptation that should be tested in other

species.

K2016

Session 2

Presentation 3

(14:00-14:15)

Biochemical and Microbial Change in Food Fermentation ‗Ubi Karet

Busuk‘ Sumba, East Nusa Tenggara, Indonesia

Periskila Dina Kali Kulla and Endah Retnaningrum

Universitas Gadjah Mada, Indonesia

Abstract—Ubi karet busuk is a traditional food fermentation product from

Sumba, East Nusa Tenggara, Indonesia which uses cassava as a substrate.

This substrate was fermented spontaneously by indigenous microorganisms

(bacteria, yeast, fungi). During the fermentation, these microorganisms

released enzymes such as amylase, protease, lipase, and hydrolyzed

polysaccharides, proteins, lipids into digestible products with a pleasant,

attractive taste and texture for human consumption. The biochemical aspects

were investigated during the fermentation, including reducing sugar, protein,

lactic acid, and pH values. The results of observations show that the bacteria

that are primarily involved in the fermentation process are lactic acid

bacteria. 15 isolates were identified phenotypically as lactic acid bacteria.

The results showed that reducing sugar levels increased from 0.09 to 0.60

mg/mL, protein levels increased from 0.08 to 0.27 mg/mL, lactic acid

increased from 1.08 % to 11.88 %, pH decreased from 6.9 to 3.8.

K0013

Session 2

Presentation 4

(14:15-14:30)

Computer Administered Banana Flour Processing System

Gamaliel Eve R. Minggong, Arjay D. Pabalinas, Hadassah Alysson F. Tesoro,

Randy E. Angelia and Hanna Leah P. Angelia

University of Mindanao, Philippines

Abstract—Class C Cavendish Bananas or Musa cavendischii though are

mostly deemed worthless, can be processed into food-grade banana flour.

This can be done through dehydration and pulverization. Some institutions

have been doing this but lacks the efficiency of automated technology for

they have been making use of manual dehydration processes like sun drying


- 39 -

& household oven drying, resulting into massive longevity of banana flour

processing. The developed system is an automated integration of

microprocessor into mechanical machinery, processing bananas into banana

flour with an interactive user interface—efficiently speeding up the usually

manual process with a single click. Reducing the dehydrating time

exponentially from 24-48 hours to 2 & a half hours at 90° C and the milling

time was timed at 3 minutes at the most for 1 kg of peeled input, showing

the efficiency of the processing system through the statistical analysis of the

data gathered from multiple testing. Therefore, the system is capable of

providing the promised improvement in the efficiency of banana flour

production.

K0017

Session 2

Presentation 5

(14:30-14:45)

Metastatic State of Colorectal Cancer can be Accurately Predicted with

Methylome

Somayah Albaradei, Maha Thafar, Christophe Van Neste, Magbubah

Essack and Vladimir B. Bajic

King Abdullah University of Science and Technology, Saudi Arabia

Abstract—Colorectal cancer (CRC) appears to be the third most common

cancer as well as the fourth most common cause of cancer deaths in the

world. Its most lethal states are when it becomes metastatic. It is of interest

to find tests that can quickly and accurately determine if the patient has

already developed metastasis. Changes in methylation profiles have been

found to be characteristic of cancers at different stages and can therefore be

used to develop diagnostic panels. We developed a deep learning (DL)

model (Deep2Met) using methylation profiles of patients with CRC to

predict if the cancer is in its metastatic state. Results suggest that our

method achieves an AUPR and an average F-score of 96.99% and 94.71%,

respectively, making Deep2Met potentially useful for diagnostic purposes.

The DL model Deep2Met we developed, shows promise in the diagnosis of

CRC based on methylation profiles of individual patients.

K4012

Session 2

Presentation 6

(14:45-15:00)

QCKer: An x86-AVX/AVX2 Implementation of Q-gram Counting Filter for

DNA Sequence Alignment

Joven L. Pernez Jr., Roger Luis Uy, Kaizen Vinz A. Borja and Jan Carlo G.

Maghirang

De La Salle University, Philippines

Abstract—The paper presents the implementation of the q-gram counting

filter using x86-AVX/AVX2 SIMD instructions. There are three novel

findings during the course of the research work. First, to eliminate

inconsistency between the theoretical and experimental result, synthetic

reads are generated using DNA character ―T‖ only since generated synthetic

reads create a random condition in which the number of seed instances is

variable, and thus cannot be predicted. Second, the presence and absence

of various SIMD parameters namely, prefetch, multithreading and AVX

instruction sets are introduced to determine the speed factor. Result shows


- 40 -

that there is a 2% speedup with the presence of prefetching, a 2.7% speedup

with the presence of AVX instruction sets, a 100.41% speedup with the

presence of multithreading, and a 112.25% speedup if all parameters are

used. This shows that multithreading has the biggest effect among the said

parameters. Third, the x86-AVX is compared with Razers3, an existing read

mapper using q-gram counting filter. In terms of filter only, the x86-AVX

is 12x faster than the Razers3 for small seed size of 4. Though, Razers3

outperforms the x86-AVX implementation for longer seed (i.e., seed size of

12). This is attributed to Razers3 being optimized for q-gram of 12 or

higher. From these findings, it is recommended that using real datasets is

preferred over synthetic datasets. Also, implementation using

multithreading approach is recommended. Though future work can be

done to compare multithread with FPGA implementation.


- 41 -

Session 3




Time: 15:15-16:45

Venue: International Meeting Room

Topic: “Statistical Genetics”

Session Chair: To be added

K1020

Session 3

Presentation 1

(15:15-15:30)

Molecular Classification of Transcriptome Expression in Serous Ovarian

Cancer using Unsupervised Clustering

Jisun Lim, Taesung Park

The Research Institute of Basic Sciences, Seoul National University, Seoul,

Korea.

Department of Statistics, Seoul National University, Seoul, Korea.

Abstract—Differentially expressed mRNAs have been found to be

associated with in the development and progression of cancer. In order to

improve chemotherapeutic treatment in serous ovarian cancer, it is needed to

identify suitable biomarkers and potential drug targets. Transcriptome

expression data obtained by RNA sequencing are analyzed. We use an

unsupervised clustering technique, such as nonnegative matrix factorization

(NMF) to identify subtypes of ovarian cancer. We demonstrate results of

NMF and discovered patterns of gene expression in hidden biological

mechanism. We further identify potential mRNA biomarkers for predicting

survival.

K1021

Session 3

Presentation 2

(15:30-15:45)

Hierarchical Component Models of Pathway Analysis for RNA Sequencing

Data

Lydia Mok, Sungyoung Lee, Taesung Park

Interdisciplinary Program in Bioinformatics, Seoul National University,

Seoul 08826, South Korea.

Center for Precision Medicine, Seoul National University Hospital, 101

Daehak-ro Jongno-gu, Seoul, South Korea

Department of Statistics, Seoul National University, Seoul 08826, South

Korea.

Abstract—In the recent years, technical improvements and decreasing costs

of next - generation sequencing technology made RNA sequencing

(RNA-seq) an alternative to the microarrays. Many number of methods and


- 42 -

software were proposed for identification of differentially expressed genes

(DEGs). However, analyzing high-throughput gene expression data at the

pathway level can be effective. Identifying active pathways that differ

between two conditions can have more explanatory power than a simple list

of DEGs. Several analyses for identifying cancer-associated pathways based

on gene expression data are mostly based on single pathway analyses, and

thus do not consider correlations between pathways. In this study, we

propose a hierarchical structural component model of pathway analysis for

RNA sequencing data for binary phenotype which accounts for the

hierarchical structure of genes and pathways in single model considering the

correlations among pathways simultaneously. The main goal of this study

was finding significant pathways that are relevant to the diagnosis of cancer.

In application to a real biological data analysis, we demonstrated that our

method could successfully identify pathways associated with diagnosis of

cancer.

K1022

Session 3

Presentation 3

(15:45-16:00)

Hierarchical Structural Component Model with 3-layers for

SNP-gene-pathway Analysis

Nan Jiang, Sungyoung Lee, Heungsun Hwang, Taesung Park


Seoul 08826, Korea

Center for Precision Medicine, Seoul National University Hospital, 101

Daehak-ro Jongno-gu, Seoul, South Korea

Department of Psychology, McGill University, 2001 Avenue McGill

College, Montreal, Quebec H3A 1G1, Canada

Department of Statistics, Seoul National University, Seoul 08826, Korea

Abstract—For genome-wide association study (GWAS), gene-based and

pathway-based analyses of common variants have been widely used to

enhance interpretation of the phenotype-related genetic variants. However,

most of these methods often neglect the SNP-gene-pathway process and

separately identify the related genes and pathways using SNP data. In this

study, we constructed a hierarchical component model that consists of

3-layers to represent the SNP-gene-pathway process. In this model,

pathways are defined as a weighted component of a set of genes, and genes

are defined as a weighted component of a set of SNPs. This model analyzes

all SNPs, genes and pathways simultaneously by ridge-type penalization of

the SNP, gene and pathway effects on the phenotype. Statistical significance

of the SNP, gene and pathway coefficients can be examined by permutation

tests. We applied our method to a SNP chip dataset of KARE for type 2

diabetes. The results showed that our method could successfully identify

signal pathways with superior statistical and biological significance. Our

approach has the advantage of providing an intuitive biological

interpretation for associations between common variants and phenotypes.


- 43 -

K1023

Session 3

Presentation 4

(16:00-16:15)

Predicting Individual Risk of Malignancy in the Patients with Intraductal

Papillary Mucinous Neoplasms of the Pancreas using Automated Machine

Learning

Chanhee Lee, Hae Seung Kang, Jin-Young Jang, Taesung Park


Seoul, Korea

Department of Surgery and Cancer Research Institute, Seoul National

University College of Medicine, Seoul, Korea

Department of Statistics, Seoul National University, Seoul, Korea

Abstract—Intraductal papillary mucinous neoplasms (IPMN) are

premalignant lesions of the pancreas. Although clinical guidelines were

released in 2012 to improve diagnosis, treatment for IPMN, due to

somewhat vague terminology and insufficient data, classifying IPMN and

assigning individual risks of malignancy to each patient remain unclear. To

evaluate individual risk of malignancy and to classify IPMN into benign or

malignant groups, we used large database of 3,464 patients from 31 different

hospitals, both Asian and Western cohorts. This study was a multi-national

(Korea, Japan, United States, China, Sweden, and Taiwan) retrospective

study. We then used automated machine learning to choose the best machine

learning algorithm for classifying IPMN patients. Most nomograms

predicting malignant intraductal papillary mucinous neoplasm (IPMN) of

pancreas were developed based on the logistic regression (LR) analysis. Six

algorithms of ML (XG boost, deep learning, distributed random forest,

generalized linear mode, gradient boosting machine, and stacked ensemble)

were utilized and compared. The algorithm which had the best performance

was selected. This study was to develop a nomogram using machine

learning (ML) and compare the performances between ML and LR model.

The performance using ML was valid in clinical circumstances

K1024

Session 3

Presentation 5

(16:15-16:30)

The Predictive Model using Extracellular Vesicles (EVs) Microbiome

Successfully Predict Matched Pancreatic Ductal Adenocarcinoma (PDAC)

and Non-cancerous Sample

Kyulhee Han, Nayeon Kang, Jae Ri Kim, Jin-Young Jang, Taesung Park


Seoul, South Korea

Department of Surgery and Cancer Research Institute, Seoul National

University College of Medicine, 101 Daehak-ro, Chongno-gu, Seoul,

110-744 South Korea

Department of Statistics, Seoul National University, Seoul, South Korea

Abstract—Pancreatic Ductal Adenocarcinoma (PDAC), the most common

type of pancreatic cancer is one of the deadliest cancer that shows poor

prognosis. Most of PDAC patients are diagnosed their disease in advanced

stage, because most PDAC cases are asymptomatic in early stage.

Therefore, it is urgent that find the early detection method of PDAC to


- 44 -

improve the overall survival of patients. Within many kind of possible target

– including genetic predisposition, toxic chemical, bacterial infection, and

etc. –, we hypothesized microbiome could be used in prediction of PDAC.

We used extracellular vesicles (EVs) metagenome data to select significant

markers and build a prediction model. Among the 87 PDAC (case) and 151

non-cancerous (control) samples, we selected 50 case and 67 control

samples using Propensity Score Matching (PSM). Several statistical

methods were applied to find differential abundance in both of phylum (L2)

and genus (L6) level. We found the 2 markers (Verrucomicrobia,

Actinobacteria) in phylum level, and 7 markers (Akkermansia,

Propionibacterium, Sphingomonas, Lactobacillus, etc.) in genus level. Our

prediction model shows higher auc than 0.8 both on phylum (0.813) and

genus level (0.879) in testing set. In conclusion, we suggest EVs

metagenomes could be used for early detection of PDAC patients.

K2015

Session 3

Presentation 6

(16:30-16:45)

DrugCell: A Visible Neural Network to Guide Precision Medicine

Kuenzi BM, Park J, Fong S, Ma J, Kreisberg JF and Ideker T,

University of California San Diego, USA

Abstract—The rate of successful translation from the bench to the bed has

not been satisfying. Many factors contribute to this problem but in most

cases, failure occurs due to the lack of understanding of how a cancer cell

responds to a particular drug. There has recently been a great deal of interest

in applying the staggering advances in artificial intelligence (AI), deep

learning in particular. However, deep learning-based models suffer from a

fundamental pitfall: these models lack interpretability as their internal

structures are ―invisible‖. To address this challenge, we developed a ―visible

neural network‖, which not only predicts anti-cancer drug response but also

allows for in silico predictions of the underlying molecular events driving

therapeutic responses. The interpretability of our prediction is achieved by

simulating the impact of genomic variations on cancer cells through an

embedded cancer cell hierarchy. Our findings are consistent with the

published synergistic drug pairs and the results of in-house CRISPR/Cas9

mediated knockout experiments. When applied to AML patient samples, our

model highlights pathways that include synergistic drug targets. The visible

AI paves the way for the next generation of intelligent systems in drug

discovery, contributing to the development of novel therapeutic solutions to

tumors.


- 45 -

Session 4




Time: 15:15-16:45

Venue: Room 105

Topic: “Biomedical Engineering”

Session Chair: To be Added

K4013

Session 4

Presentation 1

(15:15-15:30)

An EEG-based Depression Detection Method using Machine Learning

Model

Ran Bai, Yu Guo, Xianwu Tan, Lei Feng and Haiyong Xie

National Engineering Laboratory for Public Safety Risk Perception and

Control by Big Data (NEL-PSRPC), China

Abstract—Depression, different from usual mood fluctuations and

short-lived emotional responses to challenges in everyday life, is a common

illness worldwide, with more than 300 million people affected. Although

there are known, effective treatments for depression, fewer than half of

those affected in the world (in many countries, fewer than 10%) receive

such treatments. The diagnose of depression is usually subject to doctors due

to the lack of biomarkers of depression. Electroencephalogram (EEG) is an

easy-to-use, cost-effective technique that records electrical activity in brain.

In this study, 64-channel EEG data was collected from 213 subjects

including 71 health controls and 142 depression patients. 13 different

features were extracted from EEG signals from all 7 sub-bands of all

channels. 3 different feature selection models were used to find the subset of

features that best represents the characteristics of EEG signal and 6 machine

learning models were applied on all subsets of features to find the model

that gained the highest accuracy and recall on depression detection.

K4020

Session 4

Presentation 2

(15:30-15:45)

Identification of Raw EEG Signal for Prosthetic Hand Application

Azizi Miskon, Ayu Kusuma Sari Djonhari, Satria Mohd Haziq Azhar,

Suresh A/L Thanakodi and Siti Nooraya Mohd Tawil

National Defence University of Malaysia, Malaysia

Abstract—This paper presents the identification of raw

Electroencephalograph (EEG) signal for prosthetic hand application. The

main aim of this study to identify the EEG signal from human brain in real

time using Emotiv headset to control the prosthetic hand. Emotiv Epoc+

headset, Arduino Microcontroller and Prosthetic hand were the main

equipment used in this work. The prosthetic hand movement in this work


- 46 -

subjected only for opening and closing hand operation. This paper focuses

on analyzing two different methodology of prosthetic hand controlling

technique which is using the hand movement and facial expression

technique. This study able to conclude that the raw EEG signal data

obtained from facial expression method using eye blinking technique shows

better performance in real-time for software and prosthetic hand integration

by generating signal voltage peak more than 5000 µV compared to the usage

of EEG data obtained from just hand movement technique.

K4024

Session 4

Presentation 3

(15:45-16:00)

Spatio-temporal Pattern Analysis for EEG Classification in Rapid Serial

Visual Presentation Task

Bowen Li, Zhiwen Liu, Xiaorong Gao and Yanfei Lin

Beijing Institute of Technology, China

Abstract—This study will explore an algorithm of spatio-temporal pattern

analysis for electroencephalographic (EEG) classification in the rapid serial

visual presentation (RSVP) task. In this algorithm, the spatial low-rank and

temporal-frequency sparse priors are exploited to train the supervised spatial

and temporal filters. The discriminant features are extracted by the

supervised spatio-temporal filters and classified by support vector machine.

The EEG signals were recorded from a total of 12 subjects under RSVP task

and were used as training and testing data. The average true positive rate of

classification is 79%, and the average false positive rate is only 3.4%. The

classification results show that the proposed algorithm has better

performance in the target detection than HDCA and SWFP.

K0011

Session 4

Presentation 4

(16:00-16:15)

Development of Arduino Microcontroller-based Safety Monitoring

Prototype in the Hard Hat

Robert D. Arcayena Jr, Alessis D. Ballarta, Kendall N.Claros and Rodrigo S.

Pangantihon Jr.


Abstract—Construction, being one of the most dangerous sectors in the

industry, comes with fortuitous or inevitable accidents. Occupational

injuries like falling from heights, being hit by falling or moving objects,

fatigue related complications, and heat induced illnesses cause construction

losses. Despite common safety protocols, construction workers still have a

chance of 1-in-200 of dying on the job within the span of a 45- year career

making safety an issue of paramount importance for construction contractors

to monitor and manage. The main objective of this study is to ergonomically

design a hard hat with biometric sensors, an accelerometer, a GPS module,

transceivers, a fingerprint scanner, and an emergency button, all connected

to Arduino Uno Microcontroller. It monitors the biometrics of the worker,

detect external impact forces, know the location, and send distress alert

signals during emergencies. By creating a software that presents the

wearer‘s profile with the gathered data, information generated are verified

through secondary equipment for necessary calibrations. The prototype was


- 47 -

ergonomically designed with a reliable overall performance. Pulse and

temperature monitoring acquired overall accuracies of 95.62% and 99.36%,

distress alerting with 95%, impact detection, location identification, and

fingerprint scanning got 100% through performance analysis.

K4009

Session 4

Presentation 5

(16:15-16:30)

Improvement of the BT-Heartomotive Device for Avert Car Accident using

MYBradyTachyHeart Mobile Application

Mohd Azrul Hisham Mohd Adib, Muhammad Irfan Abdul Jalal and Nur

Hazreen Mohd Hasni

Universiti Malaysia Pahang, Malaysia

Abstract—Nowadays, the pulse oximeter used in the medical device is a

non-invasive sensor capable for monitoring the blood's oxygen saturation. It

has been widely used in medical, fitness and clinical care. The prototype

development of brady-tachy heart automotive so-called BT-Heartomotive

device is well developed. This device purposely to prevent motor vehicle

accident using the oximeter sensor. In this study, we focus on enhancing the

BT-Heartomotive device to preventing the car accident by using a mobile

application. The emergence of wearable sensor and wireless mobile

technologies enable to detect and monitor the changes in health parameters

irrespective of places and time. It will be much more convenient for the

patient to do a self-test diagnosis by using a wireless heart monitoring

device. The BT-Heartomotive device is simple, easy to use, low cost,

automated and provides reliable heart rate monitoring result. This kind of

real-time assistive medical diagnosis system consists of a pulse oximeter

sensor. The heart disease can be detected if the threshold value of the heart

rate is maximally exceeded. The pulse sensor and mobile apps. is connected

wirelessly via Bluetooth module. Then, the pulse sensors used for

transmitting the heart rate signals to the mobile apps. and monitor device.

These mobile apps. used for monitoring purpose to display the patient‘s

heart rhythms on the screen of the phone. The driver can observe their heart

rhythms easily by using this mobile app. This device also alerts the

passenger to quickly attend to help the driver. The device shows good

accuracy in the detection of the heart rate level. Heart rate measurement can

reveal a lot about the physical conditions of an individual.

K1004

Session 4

Presentation 6

(16:30-16:45)

Contributions of Novel Nanomaterials to Pharmaceutical Analysis

Yixin Zhang

The Taft School, USA

Abstract—This paper is an analysis of the application of nanomaterials in

pharmaceutical situations. The paper discusses the contribution of

nanomaterials in liquid chromatography - mass spectrometry (LC-MS),

capillary electrophoresis (CE), and chiral separation. Numerous researches

demonstrate the positive impact of different types of nanomaterials in a wide

range of areas, thus showing their promising future for medical purposes.

Moreover, some researches mentioned in this paper are the first reported


- 48 -

cases of their areas, indicating that more research needs to be done in order

to prove the stability of nanomaterials in certain situations. Overall, the

combination of nanotechnology and medical analysis is very efficient, which

means this field is highly valuable for further study.


- 49 -

Session 5




Time: 17:00-18:30

Venue: International Meeting Room

Topic: “Bioinformatics”

Session Chair: Assoc. Prof. Yoichi Murakami

K4014

Session 5

Presentation 1

(17:00-17:15)

Automated SNOMED CT Mapping of Clinical Discharge Summary Data

for Cardiology Queries in Clinical Facilities

Abdul Aziz Latip, Ma. Stella Tabora Domingo, 'Ismat Mohd Sulaiman and

Tengku Nurulhuda Tengku Abd Rahim

MIMOS, Malaysia

Abstract—Heart disease has remained the leading cause of death among

Malaysians for 13 years from 2005 to 2017 [1]. As it has become the

prominent factor of death in Malaysia, the intention is to improve the

accuracy of query for cardiology related cases as it is the primary source of

analytical data for heart disease. Choosing the right terminology is one of

the criteria to improve the accuracy as the clinical term can be mapped as

much as possible. Therefore, Systematized Nomenclature of Medicine

Clinical Term (SNOMED CT) has been selected for implementation as it is

known as the most comprehensive, multilingual clinical healthcare

terminology in the world. This paper presents the implementation to enrich

and increase the result accuracy by automatically mapping the Clinical

Discharge Summary using several techniques in Natural Language

Processing (NLP) with SNOMED CT. By observing the trend and pattern of

data, a facility or ministry can plan one step ahead, through prevention or

future planning. Therefore, the accuracy of the result is the key factor to

derive the outcome.

K4008

Session 5

Presentation 2

(17:15-17:30)

Acceptability of Virtual Reality among Older People: Ordinal Logistic

Regression Study from Taiwan

Diana Barsasella, Shankari Priya Chakkaravarthi, Hee-Jung Chung, Mina

Hur, Shabbir Syed Abdul, Shwetambara Malwade, Chia-Chi Chang, Megan

F. Liu and Yu-Chuan Li,

Taipei Medical University, Taiwan

Abstract—By 2050, it is estimated that 80% of older people will be living in

low- and middle-income countries. The older people are seen to limit their

physical activity after the age of 65 years. The aim of this study was to

explore the influence of sex and age towards active ageing to answer and


- 50 -

agree a usability and acceptance of Virtual Reality (VR). This pilot study

involved 30 older people who voluntarily participated in March to May

2018. They were asked to use VR for 15 minutes twice a week for 6 weeks.

We used ordinal logistic regression to see whether sex and age influenced

the answer in the Technology Acceptance Model (TAM) questionnaire.

SPSS vers.21 was used to perform statistical analyses of the data. We found

that most of them have agreed to the acceptance of VR use for each

variables of according to the sex and age.

K1010

Session 5

Presentation 3

(17:30-17:45)

Identification of Key Genes Associated with Kidney Cancer Through

Pan-cancer Bioinformatics Analysis

Nur Ain Rodzi and Suresh Kumar

Management & Science University, Malaysia

Abstract—Kidney Cancer is also known as Renal Cell Carcinoma (RCC) is

the most common and most lethal renal malignant tumour in adults. RCC

incidence levels have been reported to increase in both men and women.

Differentially expressed genes associated with kidney cancer were obtained

from the HIVE Lab (High-performance Integrated Virtual Environment)

Database were analysed. Key genes related to the pathogenesis and

prognosis of RCC were identified by employing protein–protein interaction

network. We identified ten hub genes of upregulated (EDN1, CDC25C,

P30273, LPAR5, SNAP25, GBP1, CTLA4, PECAM1) and downregulated

(PVALB, PRL, FAIM2, ATP12A, FSHB, TAC1, PENK, AFP, KCNJ9,

OCLN) of differentially expressed genes. The gene enrichment reveals

upregulated genes involved in positive regulation of renal sodium excretion,

cell surface receptor signaling pathway,plasma membrane, hormone activity

and pathway invovled in G alpha (q) signalling events. The down-regulated

genes involved in potassium ion import, neuropeptide signaling and

pathway invovled in Cell adhesion molecules and Leukocyte

transendothelial migration. The findings of this study would provide some

directive significance for further investigating the diagnostic and prognostic

biomarkers to facilitate the molecular targeting therapy of RCC.

K0008

Session 5

Presentation 4

(17:45-18:00)

Visualization of Differential Arm-specific miRNA Expression with TCGA

Dataset

Chao-Yu Pan and Wen-Chang Lin

Academia Sinica, Taiwan

Abstract—microRNAs play important regulatory roles in cellular functions

and developmental processes. They are also implicated in human

oncogenesis processes and could serve as potential cancer biomarkers. We

have been working on the discovery of miRNAs using computational

pipelines as well as NGS sequencing data. In previous studies, we

established comprehensive 5p-arm and 3p-arm miRNA annotations and

applied them for thorough interrogation on the arm-specific miRNA cancer

expression profiles. We utilized The Cancer Genome Atlas (TCGA) miRNA


- 51 -

expression datasets and explored the 5p-arm / 3p-arm miRNAs differential

expression patterns. Following statistical analysis, differentially expressed

5p-arm / 3p-arm miRNAs could be identified in various cancer types. We

identified several miRNAs significantly modulated in each cancer types.

This implicated the significance of these miRNAs in the oncogenesis

processes and could server as universal human cancer biomarkers. We then

established interactive web resource to assist biologists exploring the unique

expression profiles of individual miRNAs in different cancer types. Our goal

is to better visualize the miRNA expressions using visual analytics

techniques mainly based on the D3 JavaScript libraries. By using advanced

interactive visual user interface, our web tools could allow users to learn

more about multidimensional miRNA expression data in TCGA.

K0030

Session 5

Presentation 5

(18:00-18:15)

The Method of Organizing a Service-oriented User Interface for Multi-agent

Information and Control Systems

Iakov S. Korovin, Donat Ya. Ivanov and Sergei A. Semenistyi

Southern Federal University, Russia

Abstract—In this paper we describe a proposed method for organizing a

service-oriented interface for multi-agent information and control systems,

focused on solving large-scale problems, based on distributed computing.

We dwell on the detailed description of the architecture and elements of the

user interface, based on the structure of the solved task parameters. The

translation of these descriptions into a graphical representation within the

framework of a dynamically generated visual shell of the user is proposed.

K0031

Session 5

Presentation 6

(18:15-18:30)

Implementation of Fingerprint Recognition using Convolutional Neural

Network and RFID Authentication Protocol on Attendance Machine

Maredi Aritonang, Irwan Doni Hutahaean, Hasudungan Sipayung and Indra

Hartarto Tambunan

Institut Teknologi Del, Indonesia

Abstract—The attendance machine is a machine that can record a person's

attendance data at an institution or office that applies it. The current

attendance system is considered to be less effective because it still

implements a manual system that has weaknesses in its use, such as many

paper usage and opening gaps to falsify data. One effort to solve this

problem is to use fingerprint and RFID attendance machine. In this research,

the fingerprint grouping is performed, so the counterfeit presence data can

be minimized due to the unique and identical fingerprint pattern. The

process of grouping fingerprint images requires an approach that uses the

convolutional Neural Network (CNN) algorithm because of the difficulty of

distinguishing fingerprint patterns. Based on the results of the

implementation, it has managed to obtain an accuracy rate of 99.64%,

validation accuracy of 99.83%, and a loss of 0.001% against 2 image

classes. The attendance machine also uses RFID technology as an

alternative if the fingerprint system is experiencing interference. In this


- 52 -

research, the RFID authentication process uses the RC4 (Rivest Code 4)

cryptographic algorithm to encrypt the Unique Identifier (UID) of the

student card. Attendance machine built using Raspberry Pi 3 microcontroller

integrated with the GT-521F52 type fingerprint sensor and RFID RC522.

The system saves the recorded data in the database and connects to server so

that it can be accessed by the user via the website.


- 53 -

Session 6




Time: 17:00-18:30

Venue: Room 105

Topic: “Image Analysis”

Session Chair: Assoc. Prof. Azizi Miskon

K0003

Session 6

Presentation 1

(17:00-17:15)

Identification and Classification of Export Quality Carabao Mangoes

Johannie Ave P. Ardepolla, Mike Jhon Reymar Cortez, Abigail L. Escorpion,

Jetron J. Adtoo and Kimberly M. Nepa


Abstract—Mangifera Indica or most locally known as carabao mango is the

most commonly used variety being used for case sampling in the evaluation

of automatic mango grading system. As usually practiced, the quality of

mango is being assessed by its physical look and texture. Nowadays, the

utilization of scientific strategy for quality evaluation of mango is done

through image processing and machine learning, which is more efficient,

non-destructive and cost-effective grading method. Classified sample

carabao mangoes from a mango export Company were analyzed and

become data sets of the device. Carabao Mangoes are classified to be

Export Quality, Reject Quality and Unknown. In this paper, proposed

methodology is divided into three parts namely: (i) identifying the color of

the mangoes through RGB color recognition, (ii) grading of mango based on

its weight, (iii) determining the size of the mango by its height and width.

Functionality test and statistical analysis revealed 90 percent overall

accuracy of the device.

K0009

Session 6

Presentation 2

(17:15-17:30)

A Supervised Learning Approach on Rice Variety Classification using

Convolutional Neural Networks

Louie John L. Castillo, Juvy Amor M. Galindo and Jamie Eduardo C.

Rosal

Cor Jesu College, Philippines

Abstract—This project is about developing a portable imaging system using

Raspberry Pi that can obtain rice grain visual parameters datasets for Image

Processing. The system aims to identify and classify at least three (3)

Specific Rice varieties; use supervised learning approach on Convolutional

Neural Networks (CNN) to automatically attain the results in real-time.

CNN is a powerful algorithm to classify images. CNN was used preferably,

since other Artificial Neural Networks types needs to obtain several


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numerical parameters to be trained, and relatively require more human

efforts. The accuracy of the developed system for novel rice grain samples

have been tested for above 90% percent. The device captures the rice image

using a Raspberry Pi Camera, the captured image are then processed. 500

individual images of rice grains per variety are trained in the CNN model

and 50 epochs were used to ensure better accuracy. Apart from the 3 tested

varieties, new varieties can still be trained and tested in the CNN. The

device can assess physical and visual features of the rice. Other features

such as chemical and genetic traits are not detectable in the system.

Philippine local government does not a better materials to properly ensure

and the authenticity of the rice varieties sold in sold in the local market. This

device can help classify rice grains since there are no currently no easy

method and inexpensive tools for easily and conclusively classifying rice

grains because of the subtle its differences.

K0010

Session 6

Presentation 3

(17:30-17:45)

De-husked Coconut Quality Evaluation using Image Processing and

Machine Learning Techniques

Tito C. Lim Jr., Jaedy O. Torregosa, Aubrey Rose A. Pescadero and Rodrigo

S. Pangantihon Jr.


Abstract—Qualitative evaluation provides the basis for determining if the

quality of products meets the target specifications. Manual evaluation of

de-husked coconuts is still being performed by coconut farmers, however, it

is time consuming and costly. Ergo this study aiming to replace the manual

inspection, a prototype was developed for objective and automated quality

evaluation of de-husked coconuts through the application of computer vision

and machine learning, identifying good-quality de-husked coconuts from

defective ones with respect to its RGB color space. JavaFX platform was

utilized to create the system performing K-Nearest Neighbor and Arduino

technology played a significant role in the hardware control of the device.

The image samples were captured by a CMOS camera in an imaging

chamber with invariant illumination on top of a conveyor belt. Image

processing is done to get the required features of the sample and by

comparing the average RGB value from the custom dataset, then the

maturity level of the coconut is determined. With the accuracy of 86.667%,

the system is able to evaluate de-husked coconuts which are good for further

processing used in export and premature coconuts that are to be rejected.

K0018

Session 6

Presentation 4

(17:45-18:00)

Data Mining of Daily Pig Behaviors using Wireless IC Tag based

Monitoring System in Pig Farms

Geunho Lee, Atsushi Ishimoto, Shinsuke H. Sakamoto and Seiji Ieiri

University of Miyazaki, Japan

Abstract—Automatic sorting systems (afterward, auto sorter) used in pig

farms have been mainly manufactured and used in European countries and

America. However, from the viewpoint of pig welfare and growth


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performance, no scientific information exists about the auto sorters to direct

stockbreeders. As considering these situations, our research aims to

investigate any influences on daily pig behaviors caused by the auto sorter

used in the pig farm. Under this research direction, our paper tackles what

kind of a sensor will be used and how to collect biological data for the pigs

toward the application to a large pig farm. As a solution approach, our study

proposes a monitoring system that uses the relative received signal strength

transmitted from IC tags attached to individual pigs, enabling the system to

obtain the behavioral data such as dwell time at feeding or resting areas,

amount of movement, and so on. The implementation of the monitoring

system are explained in detail, and its effectiveness and usability are verified

through field experiments. Finally, our future work includes accessing to

information from any locations and obtaining not only behavioral

information but other biological information.

K0002

Session 6

Presentation 5

(18:00-18:15)

Supervised Machine Learning Approach for Pork Meat Freshness

Identification

Christell Faith D. Lumogdang, Christell Faith D. Lumogdang, Stephone Jone

S. Loyola, Randy E. Angelia and Hanna Leah P. Angelia


Abstract— As the number of pork consumer increases in the meat industry,

the demand for meat supplies also rises. Determining pork meat freshness,

therefore, is the primary consideration of the pork meat customers. This

smart study is mainly designed to assess and classify pork meat quality. Loin

parts weighing 100 grams from various pigs in the wet market, were

examined and became the data sets of the study, provided that a city

veterinarian has inspected and approved it. Photos of pork meat are captured

to undergo image processing. Simultaneously, electronic noses, specifically

MQ-135 and MQ-136, evaluated Ammonia and Hydrogen Sulfide

components of the pork meat, respectively. These parameters are then

classified using the k-Nearest Neighbor Algorithm. Pork meat is

distinguished from being fresh, half-fresh, and adulterated. By using the

confusion matrix principle, functionality test and statistical analysis revealed

that the system has a high accuracy rate of 93.33%.

K0004

Session 6

Presentation 6

(18:15-18:30)

Automated Vermiculture Monitoring and Compost Segregating System

using Microcontrollers

Menkent S. Barcelon, Alvin A. Orilla, Jessabelle A. Mahilum and Jetron J.

Adtoon


Abstract—Vermicomposting is the process of breaking down biodegradable

matter by earthworms to convert the contained nutrients in the organic

matter to vermicast. The study was presented by the authors to introduce the

automation system of vermiculture. By the span of 14 and 16 days only, the

conducted experiment still produced acceptable nutrient and compost


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quality for a fertilizer. Sensor readings with water sprinkler system

combination do maintain the right environment for the living conditions of

the worm. Through the use of microcontrollers Arduino and Raspberry Pi,

human intervention can be lessened and the system would be expedited if

the process of vermicomposting will be automated rather than going manual.


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Poster Session Afternoon, December 20, 2019(Friday)

Time: 15:00-15:15&16:45-17:00

Venue: Lobby of Building 25-1

K4010

Poster 1

Discrimination Colonies of Staphylococcus Aureus and Salmonella Enterica

by using Machine Learning

Manao Bunkum and SarinpornVisitsattapongse

King Mongkut‘s Institute of Technology Ladkrabang Bangkok, Thailand

Abstract—Discriminative bacteria is very important because bacteria can

contaminate in food and environment. The bacteria are the cause of some

diseases. In the present, the technique for discrimination and counting

bacteria use a lot of time and budget. Moreover, the discrimination method

has done by human who expert in that way. So, this research will create

algorithm for solve these problems by use bacteria Staphylococcus aureus

and Salmonella enterica to pilot study. The bacteria samples must be show

single colony for good detection so, this research use stab technique for each

bacterium on Luria-Bertani agar (LB agar) plates. The image segmentation

technique was used to separate colony of each bacteria for train in machine

learning algorithm. This research use sample of bacteria‘s image around 800

images for training. This algorithm can count colony of bacteria at the

accuracy of 98.75 % and discriminate Staphylococcus aureus and

Salmonella enterica at the accuracy of 98.12%.

K5002

Poster 2

The Noninvasive Blood Glucose Monitoring by Means of Near Infrared

Sensors

Jindapa Nampeng, Yanisa Samona, Chuchart Pintavirooj, Baorong Ni and

Sarinporn Visitsattapongse

King Mongkut‘s Institute of Technology Ladkrabang Bangkok, Thailand

Abstract—Diabetes is a type of metabolic disease that causes a high blood

glucose level that wildly found in many countries. Blood glucose

measurement is necessary for diabetes patients to check how much glucose

is present in the blood. The typical method to measure blood glucose level is

an invasive method that gives a highly accurate result, but the patients get

suffer from physical pains and it has a higher risk of infection. This research

presents an alternative method, which is noninvasive blood glucose

monitoring by means of Near infrared sensors based on 940nm near infrared

spectrum and an artificial neural network analysis. The concept is focusing

on glucose absorbance detection when the spectrum passes through the

patient‘s finger. In processing the signals, the wavelet‘s transformation is


- 58 -

selected to do signal conditioning and extract four eigenvalues. The four

eigenvalues are the key features for training the artificial neural network

analysis model that gives an efficiency prediction algorithm of blood

glucose level. The experiment shows that the accuracy of the noninvasive

method that has the approximate regression value is 0.9534. The

noninvasive blood glucose monitoring by means of Near infrared sensors

causes less pain and lower risk of infection when compared with the

invasive method.

K4018

Poster 3

In Vivo Performance and Biocompatibility of an Intelligent Artificial Anal

Sphincter System

Ding Han, Guo-Zheng Yan and Kai Zhao

Shanghai Jiaotong University, China

Abstract—Severe fecal incontinence is an embarrassing and psychosocially

debilitating condition that has a considerable negative impact on quality of

the life. This article describes an intelligent artificial anal sphincter system

(AASS) based on enteric cavity pressure signal feedback mechanism and its

in vivo experiment in two dogs. The optimized AASS consists of an external

telemetry unit, internal artificial anal sphincter (IAAS) and transcutaneous

energy transfer charging system (TETCS). The new sphincter prosthesis was

designed with pressure sensor to simulate the part function of the external

anal sphincter. The devices were implanted in two dogs and studied for

periods of up to 5 weeks. The efficacy of the device in achieving continence

and sensing the stool was assessed. The biocompatibility and biosecurity,

including blood supply of the rectum, blood serum chemistry, and histologic

examination of tissue, were evaluated during and after experiment. Results

of the chronic animal experiment demonstrated no significant tissue

inflammation. Functionality and biocompatibility of the improved device

have been proved.

K4022

Poster 4

Optimization of the Treatment of Chronic Eczema in the Elderly

Zhumash Nurmukhambetov, Torgyn Ibrayeva, Alibek Nurmukhambetov

and Yerlan Bazarbekov

Semey Medical University, Kazakhstan

Abstract—The high prevalence and social significance of eczema in the

modern world are not in doubt. According to various authors, it takes from

10% to 40% of all cases of skin diseases [1, 2, 3]. However, in the elderly,

eczema accounts for even more than 50% in the structure of skin pathology

[4]. This problem is of extreme importance in the modern world, given the

fact that, at the moment, the whole world is focused on a significant increase

in the number of the elderly. In this sense, the need for dermatological care

in people of this category is significantly higher than in people of working

age. Many treatment methods have been developed, but medical practice

urgently requires the improvement and creation of more effective therapies.


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K4025

Poster 5

The Efficacy and Safety of Long-term Aspirin Use for Cancer Primary

Prevention: An Updated Systematic Review and Subgroup Meta-analysis of

Randomized Controlled Trials

Qibiao Wu, Xiaojun Yao, Hongwei Chen and Elaine Lai-Han Leung

Macau University of Science and Technology, China

Abstract—Long-term aspirin use for primary prevention of cancer remains

controversial, and variations in the effect of aspirin use on cancer outcomes

by aspirin dose, follow-up duration, or study population have never been

systematically evaluated. This updated meta-analysis was conducted to

evaluate the efficacy and safety of aspirin use for cancer primary prevention

and determine whether the effect differed according to aspirin dose,

follow-up duration, or study population.

Seven electronic databases (PubMed, EMBASE, ClinicalTrials.gov, etc.)

were searched from inception to September 30, 2019. Randomized clinical

trials (RCTs) comparing aspirin use versus no aspirin use in participants

without pre-existing cancer that reported cancer incidence and/or cancer

mortality outcomes were selected and assessed for inclusion. Studies with a

follow-up of at least one year were eligible. Data were screened and

extracted by different investigators. The Cochrane‘s Risk of Bias Tool and

the Jadad scale were used to evaluate the risk of bias and the methodologic

quality of the RCTs. Analyses were performed using Review Manager 5.3,

Comprehensive Meta-Analysis 2.0 and Trial Sequential Analysis software

(TSA). The Grading of Recommendations Assessment, Development and

Evaluation (GRADE) working group methodology was used to assess the

strength of the body of evidence. Total cancer incidence was defined as the

primary clinical endpoint. Total cancer mortality, all-cause mortality, major

bleeding, and total bleeding events were the secondary outcomes. Subgroup

analyses were conducted based on aspirin dose, follow-up duration, and

study populations. 29 RCTs that randomized 200,679 participants were

included. Compared with no aspirin, aspirin use was not associated with

significant reductions in total cancer incidence (RR = 1.01, 95% CI 0.97 to

1.04, P = 0.72), total cancer mortality (RR = 1.00, 95% CI 0.93 to 1.07, P =

0.90), or all-cause mortality (RR = 0.98, 95% CI 0.94 to 1.02, P =0.31);

however, aspirin use was associated with a 44% increase in the risk of major

bleeding (RR = 1.44, 95% CI 1.32 to 1.57, P < 0.00001) and a 52% increase

in the risk of total bleeding events (RR = 1.52, 95% CI 1.33 to 1.74, P <

0.00001). The results were consistent when subgroup analyses were

performed based on the daily dose of aspirin, follow-up duration, and study

population. Trial sequential analysis and the meta-regression analysis

indicated that aspirin use was not significantly superior to no aspirin use,

and the total cancer incidence, total cancer mortality and all-cause mortality

rates were not reduced with a larger daily dose of aspirin or a longer

follow-up duration. Most results were robust, and the quality of evidence

ranged from moderate to high. Long-term use of aspirin in individuals


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without pre-existing cancer was not associated with a significant reduction

in total cancer incidence, cancer mortality, or all-cause mortality; however,

aspirin use was associated with a significant increase in the risk of bleeding.

Therefore, aspirin is not an appropriate choice for primary prevention of

cancer. Prospective clinical trials are warranted.

K1005

Poster 6

Rational Design of NOT-gate in Tri-node Enzyme Regulatory Networks

Xiao Wang and Xudong Lv

Shandong University at Weihai, China

Abstract— Synthetic biology shows a lot potential building biological

systems to perform various target function using basic bio-circuits as

modules. However, the design of NOT-gate, as a critical circuit component

in enzyme regulation network, has been rarely attempted. We

computationally searched all possible tri-node enzyme network topologies

to identify those who could present NOT-gate behavior. The results show

that a NOT-gate can be achieved if and only if the network contains a

(Direct or Indirect) Negative Feedforward link from Input to Output

(DNFIO/INFIO). Furthermore, we discovered a negative feedback on input

node improves NOT-gate performance. The minimal DNFIO motif was

analytically interpreted, matching with our computational results. This study

adds a curial component to the design toolbox of synthetic biology and

paves the way for deeper understanding negative feedback systems such as

blood glucose regulation.

K2007

Poster 7

Genetic Mutations Associated with Diffuse Large B-cell Lymphoma

Jinghan Qiu

Rutgers Preparatory School, USA

Abstract—Diffuse Large B-Cell Lymphoma (DLBCL) is the most common

non-Hodgkin lymphoma (NHL) among adults. [1] A cancer of B cells,

DLBCL can arise in any part of the body. [2] Although DLBCL is not well

understood and classified right now, substantial credible clinical data took

by authoritative organizations are available online. Online clinical data

related to DLBCL include data of mutated gene, high sequence, gene

expression, copy number, etc. Here, utilizing clinical data, the researcher

finds the most frequently mutated genes, and by analyzing the

llluminaHiSeq in TCGA DLBCL data set, the researcher finds eleven

frequently mutated genes that have significant effect on certain genes‘

expression once mutated (ARID1A, HIST1H1E, MGA, ATM, SGK1, IRF8,

TET2, BTG2, EP300, CHD8, MLL2). If these eleven genes‘ mutation can

be controlled by medical therapies, patients with DLBCL may be treated

because of the reducing irregular gene expressions.

K4006

Poster 8

Comparison of Two Different Kernel Functions of Support Vector

Regression for Tracking Tumor Motion: Radial Basis Function and Linear

Function

Jie Zhang, Xue Bai and Guoping Shan


- 61 -

Zhejiang Cancer Hospital, China

Abstract—tumor between two different kernels of support vector regression

(SVR). The two kernels are radial basis function (RBF) and linear function.

Methods: The comparison focused on the prediction accuracy. A RBF-based

SVR (RBF-SVR) and a linear function based SVR (Linear-SVR) were both

applied on the same bi-modal liver motion data. The data were shared on a

website. It involved 15 sets of a vessel bifurcation‘s motion and three

external skin markers‘ motion. The vessel bifurcation‘s motion was regarded

as target motion in our work. All signals were 6~20 minutes in length. To

simulate the modeling phase and predicting phase in real applications, the

first 5-minute session was used to build and train the model. The rest was

used for validation. Results: For RBF-SVR, 80% cases had a prediction

error of less than3.7mm; 90% cases had a prediction error of less than

5.6mm. Linear-SVR achieved a prediction error of less than 2.8mm for 80%

cases and a prediction error of less than 3.5mm for 90% cases. Besides,

Linear-SVR had a better root-mean-square error than RBF-SVR.

Conclusion: To predict a moving target position using the higher-dimension

traces of external skin markers, Linear-SVR can achieve a better accuracy

than RBF-SVR.

K4007

Poster 9

The Accuracy Heart Dosimetric Study of Left-breast Cancer Radio-therapy

using Deformable Image Registration

Xue Bai, Shengye Wang, Binbing Wang and Jie Zhang

Zhejiang Cancer Hospital, China

Abstract—The radiation injury of heart is an obviously risk in left-breast

cancer radiotherapy. In this study, the uncertainty of intra-fraction and

inter-fraction for heart dose was investigated using the 4DCT, CBCT and

deformable image registration (DIR) to understand the exact dose. The

secondary objective of this study was to evaluate the impact of DIR

uncertainty on dose accumulation. 4DCT and CBCT images were scanned

for ten left-breast cancer patients before and during 3D-CRT treatment. An

anatomically constrained hybrid DIR method and a biomechanical model

based DIR method were applied to dose accumulation. Dose scenarios

included plan dose (no motion), 4D dose (intra-fraction motion) and

accumulated dose (inter-fraction motion). The doses to the heart were

assessed. the differences among plan heart dose, 4D heart dose and

accumulated dose of the investigated parameters Dmean, Dmax, V10, V20, V30

and V40 were ranged -3.8~7.1%, -0.6~-0.5%, -1.3~0.8%, -0.8~1.7%,

-0.8~1.7% and -0.5~2.2% respectively. The uncertainty between the two

DIR methods were ranged -1.3~0.2% for all the investigated parameters of

heart. There was minimal uncertainty of cardiac dose in DIR algorithms,

meanwhile the intra-fraction variation was lager, and the inter-fraction

variation was the largest uncertainty.


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K4016

Poster 10

Druggability of Intrinsically Disordered Proteins and Their Virtual

Screening Strategy

Yutong Wan

New Jersey Institute of Technology, USA

Abstract—Intrinsically disordered proteins (IDPs) widely exist in nature and

have important physiological functionalities in life. They are related to

multiple human diseases. Thus, studying IDPs provides new opportunities

on drug design. This paper briefly reviewed the research progress of IDPs.

Then a new approach was introduced which utilised a software called

CAVITY to seek potentially druggable cavities in IDPs and analysed the

structural conservation of these cavities. We discover that even if IDPs lack

of stable secondary or tertiary structure, the structures of potentially

druggable cavity are still able to maintain a good consistency. Finally, this

paper discussed the possibilities of IDPs being drug design targets and the

rational strategy of drug design on IDPs.

K4019

Poster 11

Multiple Absorption Spectra Modeling Method for Improving Model

Stability in Spectral Analysis

Yongshun Luo, Gang Li and Ling Lin

Tianjin University, China

Abstract—In spectral quantitative analysis, the stability of the model

determines its application value. The stability is affected by the difference

between modeling conditions and application conditions. A multiple

absorption spectra modeling method (MASM method) for a weak scattering

solution is proposed in this study. The influence of external measurement

conditions on the robustness is illustrated by the difference in incident lights

caused by changing the positions of the light source. The MASM method

can suppress these effects and maintain a high prediction accuracy. In this

paper, a verification experiment is designed. The light source is accurately

located at three equidistant positions and the transmission spectra is

measured at four positions with equal spacing. The single absorption

spectrum modeling method (SASM method) and the MASM method are

used for modeling and analysis respectively. The results show that the

prediction accuracy of the MASM method is 40%-81.2% higher than that of

the SASM method, which proves that the MASM method has strong

robustness towards the changes in incident light intensity.


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Note


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Note

conference abstract - icbraicbra.org/icbra 2019program.pdf · welcome to 2019 6th international...

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