disease informatics: terms and jargon to begin with r. p. deolankar

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Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

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Page 1: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Disease Informatics: Terms and Jargon to begin with

R. P. Deolankar

Page 2: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

General Terms

Page 3: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Data and Information

Data• Numbers• Words• Images• Information is derived from the dataInformation• It is the knowledge derived from analysis of the data• Inferences can be drawn from information• The inferences drawn from earlier work provides the basis for

projected work

Page 4: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Target information and Information gap

Target information• Information which is required but not

available• The information goal intended to be attainedInformation gap• Total information required to hit the

information target minus available information

Page 5: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Research question and HypothesisResearch question• This is the question, if answered, could eliminate the information

gap• The cycle of setting the information target, locating the information

gap and raising new research questions is the part of process of research

Hypothesis• This is a tentative answer to the research question• The hypothesis is tested by performing the experiment• After testing, hypothesis is either accepted or rejectedPostulation• Hypothesis that cannot be tested and hence taken for granted• A statement as the basis of a theory

Page 6: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

(Disease phenomenon is the result of several causes, not just one)

Multiple hypotheses• More effective way of organizing research• Provides stimulus for study and fact-finding• See the interaction of the several causes• Promotes much greater thoroughness• Leads to lines of inquiry that we might otherwise overlook• Avoids the pitfall of accepting weak or flawed evidence for

one hypothesis when another provides a more elegant solution

Precautions• Keeping a written list of multiple hypotheses is necessary• Difficult to test• Vacillation is preferable to the premature rush to a false conclusion

Page 7: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Thomas Chrowder ChamberlinAuthor of Method of Multiple Working Hypotheses

Page 8: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

What is ontology?

• Incomplete information gives rise to speculation

• Hierarchical structuring of speculations about things within a particular domain is ontology

• Ontology is the statement of a logical theory

Page 9: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Disease Ontology

• Controlled Medical Vocabulary• Facilitate mapping of diseases and associated

conditions to codes such as ICD, SNOMED and others

• Disease Ontology (DO) is developed at the Bioinformatics Core Facility in collaboration with the NuGene Project at the Center for Genetic Medicine, USA

Page 10: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Clinical event

• Clinical: related to the health or disease• Event: something that happens at a given place

and time• Depicted at both the ends of “cause and effect

diagram”• Link of a Disease Causal Chain• Backend event: Event occurring earlier to the

focused event • Frontend event: Event occurring next to the

focused event

Page 11: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Biomarker

• Indicator of event of health / disease / clinical history

• Usually biochemical metabolite• Indicator of normal biologic processes,

pathogenic processes, or pharmacologic responses to a therapeutic intervention.

Page 12: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Disease Causal Chain

• Diagram depicting chain or net• Links of chain are events• Progress from one event to other is shown by

“Cause and effect” diagram• Journey from one event to the other is driven

by factors

Page 13: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Model organism

• Animal model in study of diseases• Discoveries made in the animal model provides

insight into the human disease study• Studies include pathogenesis, potential causes

and treatments of diseases• Basis: common descent of all living organisms,

and the conservation of metabolic and developmental pathways and genetic material over the course of evolution

• Research performed using poor quality animals could be misguiding

Page 14: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Component cause

• Belief in one cause one effect is a major error in disease investigation

• Single component cause does not result in disease

• Virus is a component cause in a viral disease• Subset of sufficient causes does not result in a

disease but could predispose • Most causes of interest to the epidemiologist

are actually components of a sufficient cause

Page 15: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Sufficient cause

• Sufficient causes are constellation of component causes that could result in a disease

• Factors contributing susceptibility to virus are also component causes of viral disease

• Disease can originate from either of several different sufficient causes

Page 16: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Book by Rothman and Greenland

Page 17: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

NCL-60 lines

• Cell lines for anticancer drug screening• Developed by the National Cancer Institute,

Maryland, USA• Reflect diverse cell lineages [lung, renal, colorectal,

ovarian, breast, prostate, central nervous system, melanoma, and hematological malignancies]

• Such panels could be prepared for other diseases also

Page 18: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Algorithm

• A precise rule or set of rules• A sequence of instructions• Specify how to solve some problem

Page 19: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Metathesaurus

• Vocabulary for information retrival• Integrated from synonyms and antonyms for

common words and phrases (thesauri)• e.g. Unified Medical Language System to

integrate into a single system the terminology of the biomedical sciences

Page 20: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

SNOMED CT and SNOMED RT

• SNOMED: Sytematized NOMencalture of MEDicine

• CT for Clinical Terms• RT for reference terminology

Page 21: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

UMLS: Unified Medical Language System

• UMLS is a metathesaurus• Developed by the National Library of Medicine

(NLM)• Contains Knowledge Sources (databases) and

associated software tools (programs)• Useful for developers of computer system

Page 22: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

UML: Unified Modeling Language Not to be confused with UMLS

• A standardized general-purpose modeling language in the field of software engineering

• UML includes a set of graphical notation techniques• Creates abstract models of specific systems• Diagrams: structure (Class, Component, Composite

structure, Deployment, Object and Package diagrams), behavior (Activity, State and Use case) and interaction (Communication, Interaction overview, Sequence and Timing)

Page 23: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Semantic Network

• Knowledge diagram with graphic notation• Looks like flow chart• Contains patterns of interconnected nodes

and arcs

Page 24: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

SPECIALIST Lexicon

• SPECIALIST is the name of Natural Language Processing (NLP) System

• Lexicon (dictionary like document) developed using SPECIALIST is SPECIALIST lexicon

• Vocabulary encompassing English and biomedical terminology

• The lexicon entry for each word or term records the syntactic, morphological, and orthographic information needed by the SPECIALIST NLP System

Page 25: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Genetic terminology

Page 26: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Essential genes

• Genes required for growth to a fertile adult• Essential for viability

Page 27: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Housekeeping genes

• Involved in basic functions needed for the sustenance of the cell

• Constitutively expressed• They are always turned ON e.g. actin

Page 28: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Disease-associated genes

• Alleles carrying particular DNA sequences associated with the presence of disease

• e.g. Gene UNC-93B deficiency as a genetic etiology of Herpes Simplex Encephalitis

• Lack of Stat1 interferon signaling gene enhances pathogenesis of a viral disease

Page 29: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Gene Ontology (GO)

• The Gene Ontology (GO) is a project• Provides a controlled vocabulary to describe

gene and gene product attributes in any organism

• (the molecular function of gene products; their role in multi-step biological processes; and their localization to cellular components)

Page 30: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Epigenetic

• Relating to, being, or involving a modification in gene expression

• It is independent of the DNA sequence of a gene

• DNA methylation, chromatin remodeling, transcription factors etc

Page 31: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Paralogs: Paralogous genes

• Two genes or clusters of genes at different chromosomal locations in the same organism

• Have structural similarities indicating that they derived from a common ancestral gene

• Have diverged from the parent copy by mutation and selection or drift.

Page 32: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Homologs: Homologous genes

• Homologs: Having the same relative position, value, or structure, something (as a chemical compound or a chromosome) that is homologous

• Homologous sequences are of two types: orthologous and paralogous

Page 33: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Orthologs: orthologous genes

• Orthologous genes: genes that have evolved directly from an ancestral gene

• This is in contrast to paralogous genes

Page 34: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Interlogs

• Suppose protein molecules (from one species of animal say human) A and B interact; homologous protein molecules (from another species of animal say dog) A’ and B’ also interact, then interlogs are:

• Resembling pair of protein-protein interactions (e.g. A-B and A'-B')

• Can be observed parallelly in two different organisms

Page 35: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Interologous Interaction Database

• Web-accessible database to facilitate experimentation and integrated computational analysis with model organism Protein-Protein-Interaction networks

Page 36: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Regulogs

• Sets of co-regulated genes for which the regulatory sequence has been conserved across multiple organisms

• The quantitative method assigns a confidence score to each predicted regulog member on the basis of the degree of conservation of protein sequence and regulatory mechanisms

Page 37: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Translational medicine: ("Bench to bedside" research)

• Clinical Research orienting interaction between basic research and clinical medicine, particularly in clinical trials

Page 38: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Systems biology

• Relatively new biological study field • Focuses on the systematic study of complex

interactions in biological systems• Uses a new perspective (integration instead of

reduction) to study complex interactions

Page 39: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Predictive medicine

• Identifying biological markers in order to enroll individuals at high risk for developing a disease in special early detection trials

Page 40: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Meta-analysis

• In statistics, a meta-analysis combines the results of several studies that address a set of related research hypotheses

Page 41: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Bayesian approach

• Statistical approach based on Bayes' theorem• Application of Baye’s theorem: Bayes'

theorem can be applied to calculate the probability that a positive medical test result of a disease is a false positive hence retesting is planned

• Bayes' theorem can be also be applied to calculate the probability of a false negative

Page 42: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Omics terms

Page 43: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Genomics

• The branch of genetics that studies organisms in terms of their genomes (their full DNA sequences)

Page 44: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Pharmacogenomics

• Study of how an individual's genetic inheritance affects the body's response to drugs

• Tailor-made for individuals and adapted to each person's own genetic makeup

• Greater efficacy and safety• Environment, diet, age, lifestyle, and state of

health all can influence a person's response to medicines

Page 45: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Nutrigenomics

• Study of molecular relationships between nutrition and the response of genes

• Personalized nutrition based on genotype

Page 46: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Phenomics

• Field of study concerned with the characterization of phenotypes

• Phenotypes arise via the interaction of the genome with the environment

Page 47: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Transcriptome and transcriptomics

Transcriptome• The complete set of RNA products (mRNAs,

or transcripts in a particular tissue at a particular time) that can be produced from the genome

Transcriptomics• The study of the transcriptome

Page 48: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Proteome and proteomics

• Proteome• PROTEin complement to a genOME• Proteomics• The qualitative and quantitative comparison

of proteomes• The comparison under different conditions to

further unravel biological processes

Page 49: Disease Informatics: Terms and Jargon to begin with R. P. Deolankar

Metabolome and Metabolomics

• Metabolome• It represents the collection of all metabolites

in a biological organism, which are the end products of its gene expression

• Metabolomics• Study of metabolome under different

conditions