editorial big data and network biology 2016downloads.hindawi.com/journals/bmri/2017/9432460.pdf ·...

EditorialBig Data and Network Biology 2016

Shigehiko Kanaya,1 Md. Altaf-Ul-Amin,1 Samuel K. Kiboi,2 and Farit Mochamad Afendi3

1Nara Institute of Science and Technology (NAIST), Nara 630-0192, Japan2University of Nairobi, Nairobi 00100, Kenya3Bogor Agricultural University, West Java 16680, Indonesia

Correspondence should be addressed to Shigehiko Kanaya; [email protected]

Received 22 December 2016; Accepted 26 December 2016; Published 26 January 2017

Copyright © 2017 Shigehiko Kanaya et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Big data science towards biology has emerged as an im-portant research and application field. The scope of bigdata bioinformatics and the need for integrated systemsbiology applications have posed significant challenges tocomputing systems. With the growing deluge of molecu-lar biological data, big data methodologies, including datamining algorithms and processing techniques, are becomingparticularly relevant. These encompass data accumulation,storage, retrieval, classification, and visualization.These havebecome increasingly important themes of research in the pastdecade. Network analysis has become popular for systemslevel analysis of biological phenomena. Both micro- andmacrolevel biological networks of various types facilitatethe development of theory and methodology for solvingcore scientific issues. The application ranges from studiesof ecological structure, biodiversity and environment, andevolution and extinction of species. It is also more widelyused in studies on metabolic regulation and biomarker iden-tification particularly for noncommunicable diseases suchas cancer, Alzheimer’s disease, and diabetes. The versatilityof these topics, theory and methodology of big data andnetwork biology, justifies the aims and scope of this specialissues. The present special issue is however not an exhaustiverepresentation of these topics.

This special issue contains six papers. One article presentsa biomedical text mining approach and another article talksabout algorithms for inferring gene regulatory networks.Four other papers present methodology related to genomics,transcriptomics, metabolomics, and drug repositioning.

The paper “Novel Approach to Classify Plants Based onMetabolite-Content Similarity” proposed an unsupervisedapproach to classify plants based on their known metabolitecontent data. Plants were classified based on structurally sim-ilar metabolite groups to reduce the influence of incompletedata. The resulting plant clusters were found to be consistentwith known evolutional relations of plants and reveal thesignificance of metabolite content as a taxonomic marker.

The paper “A Systematic Framework for Drug Reposi-tioning from Integrated Omics and Drug Phenotype ProfilesUsing Pathway-Drug Network” proposes a systematic frame-work that employs experimental genomic knowledge andpharmaceutical knowledge to reposition drugs for a specificdisease. The experimental results showed that the proposedframework is a useful approach to discover promising candi-dates for breast cancer treatment.

The paper “MapReduce Algorithms for Inferring GeneRegulatory Networks from Time-Series Microarray DataUsing an Information-Theoretic Approach” proposes newMapReduce algorithms for inferring gene regulatory net-works (GRNs) on a Hadoop cluster in a cloud environment.These algorithms employ an information-theoretic approachto infer GRNs using time-series gene expression profiles.Experimental results show that their MapReduce program ismuch faster and achieves slightly better prediction accuracythan a state-of-the-art R program.

The paper “Correlation-Based Network Generation,Visualization, and Analysis as a Powerful Tool in BiologicalStudies: A Case Study in Cancer CellMetabolism” introducesone of a series of methods for correlation-based network

HindawiBioMed Research InternationalVolume 2017, Article ID 9432460, 2 pageshttps://doi.org/10.1155/2017/9432460

https://doi.org/10.1155/2017/9432460

2 BioMed Research International

generation and analysis using freely available software. Thepipeline used publishedmetabolomics data of a population ofhuman breast carcinoma cell lines MDA-MB-231 under nor-mal and hypoxia conditions.The analysis revealed significantdifferences between the metabolic networks in response tothe tested conditions.

The paper “Semisupervised Learning Based Disease-Symptom and Symptom-Therapeutic Substance RelationExtraction from Biomedical Literature” presents a method ofconstructing two models for extracting the relations betweenthe disease and symptom, and symptom and therapeutic sub-stance from biomedical texts, respectively. The authors applytwo semisupervised learning algorithms, Co-Training andTri-Training, to boost the relation extraction performance.

Horizontal gene transfer (HGT) has had an importantrole in eukaryotic genome evolution.Thepaper “HorizontallyTransferred Genetic Elements in the Tsetse Fly Genome: AnAlignment-Free Clustering Approach Using Batch LearningSelf-Organising Map (BLSOM)” employs BLSOM to explorethe genome of Glossina morsitans for evidence of HGT frommicroorganisms. The predicted donors of HGT candidateinclude diverse bacteria that have not previously been asso-ciated with the tsetse fly. These findings provide a basis forunderstanding the coevolutionary history of the tsetse fly andits microbes and establish the effectiveness of BLSOM for thedetection of HGT.

Acknowledgments

We thank the authors of the articles in this special issuefor their contributions and their patience in communicatingwith us. Finally we acknowledge the dedicated works ofall reviewers of these papers for their critical and helpfulcomments which helped a lot in the improvement of themanuscripts.

Shigehiko KanayaMd. Altaf-Ul-Amin

Samuel K. KiboiFarit Mochamad Afendi

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