diseases
DESCRIPTION
Genes. Diseases. Diseases. Diseases. Physiology. Diseases. Physiology. Genes. Genes. Anatomy. Diseases. Physiology. Anatomy. Diseases. Physiology. Anatomy. Diseases. Physiology. Anatomy. Diseases. Physiology. Anatomy. Diseases. Physiology. Anatomy. Diseases. Anatomy. - PowerPoint PPT PresentationTRANSCRIPT
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DiseasesDiseases
DiseasesDiseasesDiseases
Diseases
Diseases
Anatomy Anatom
y Anatomy Anatom
y Anatomy
Anatomy
Gen
esG
enes
Gen
esG
enes
Gen
esG
enes
Physiology Physiolog
y Physiology Physiolog
y Physiology Physiology
Diseases
PhysiologyAnatomy
Genes
Genes
GenesDiseases
Diseases
Medical Informatics
BioinformaticsNovel relationships & Deeper insights
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04/24/23
Mining Bio-Medical MountainsAnil Jegga
Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center (CCHMC)
Department of Pediatrics, University of Cincinnatihttp://[email protected]
Integrative Genomics For Understanding Disease
Process
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AcknowledgementBiomedical Engineering/Bioinformatics• Jing Chen• Sivakumar Gowrisankar• Vivek KaimalComputer Science• Amit Sinha• Mrunal Deshmukh• Divya Sardana• Sandhya ShahdeoElectrical Engineering• Nishanth Vepachedu
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Medical Informatics Bioinformatics & the “omes
Patient Records
Disease Database→Name→Synonyms→Related/Similar Diseases→Subtypes→Etiology →Predisposing Causes→Pathogenesis→Molecular Basis→Population Genetics→Clinical findings→System(s) involved→Lesions →Diagnosis→Prognosis→Treatment→Clinical Trials……
PubMed
Clinical Trials
Two Separate Worlds…..
With Some Data Exchange…
Genome
Transcriptome
miRNAome
Interactome
MetabolomePhysiom
e
Regulome Variome
Pathome Phar
mac
ogen
ome
OMIMClinical
Synopsis
Disease
World
382 “omes” so far………and there is “UNKNOME” too - genes with no function knownhttp://omics.org/index.php/Alphabetically_ordered_list_of_omics
Proteome
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now…. The number 1 FAQHow much biology should I
know??No simple or straight-forward answer…
unfortunately!But the mantra is:
Interact routinely with biologists
ORWork with the biologists or the
biological data
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But I want to learn some basics…1. http://www.ncbi.nlm.nih.gov/Education2. http://www.ebi.ac.uk/2can/3. http://www.genome.gov/Education/4. http://genomics.energy.gov/Books1. Introduction to Bioinformatics by Teresa Attwood, David Parry-
Smith2. A Primer of Genome Science by Gibson G and Muse SV3. Bioinformatics: A Practical Guide to the Analysis of Genes and
Proteins, Second Edition by Andreas D. Baxevanis, B. F. Francis Ouellette
4. Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology by Dan Gusfield
5. Bioinformatics: Sequence and Genome Analysis by David W. Mount
6. Discovering Genomics, Proteomics, and Bioinformatics by A. Malcolm Campbell and Laurie J. Heyer
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And the other FAQs….1. What bioinformatics topics are closest
to computer science?2. Should computer science departments
involve themselves in preparing their graduates for careers in bioinformatics?
3. And if so, what topics should they cover?
4. And how much biology should they be taught?
5. Lastly, how much effort should be expended in re-directing computer scientists to do work in bioinformatics?
Cohen, 2005; Communications of the ACM
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Issues to be considered……..1. Computer science Vs molecular biology
– Subject & Scientists - Cultural differences
2. Current goals of molecular biology, genomics (or biomedical research in a broader sense)
3. Data types used in bioinformatics or genomics
4. Areas within computer science of interest to biologists
5. Bioinformatics research - Employment opportunities
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Biological Challenges - Computer Engineers
• Post-genomic Era and the goal of bio-medicine– to develop a quantitative understanding of how
living things are built from the genome that encodes them.
• Deciphering the genome code– Identifying unknown genes and assigning function
by computational analysis of genomic sequence– Identifying the regulatory mechanisms– Identifying their role in normal
development/states vs disease states
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• Data Deluge: exponential growth of data silos and different data types– Human-computer interaction specialists need to
work closely with academic and clinical biomedical researchers to not only manage the data deluge but to convert information into knowledge.
• Biological data is very complex and interlinked!– Creating information systems that allow
biologists to seamlessly follow these links without getting lost in a sea of information - a huge opportunity for computer scientists.
Biological Challenges - Computer Engineers
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• Networks, networks, and networks!– Each gene in the genome is not an
independent entity. Multiple genes interact to perform a specific function.
– Environmental influences – Genotype-environment interaction
– Integrating genomic and biochemical data together into quantitative and predictive models of biochemistry and physiology
– Computer scientists, mathematicians, and statisticians - ALL are/will be an integral and critical part of this effort.
Biological Challenges - Computer EngineersA major goal in
molecular biology is Functional Genomics
– Study of the relationships among genes in DNA & their function – in normal and disease states
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Informatics – Biologists’ Expectations
• Representation, Organization, Manipulation, Distribution, Maintenance, and Use of information, particularly in digital form.
• Functional aspect of bioinformatics: Representation, Storage, and Distribution of data.– Intelligent design of data formats and databases– Creation of tools to query those databases– Development of user interfaces or visualizations
that bring together different tools to allow the user to ask complex questions or put forth testable hypotheses.
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• Developing analytical tools to discover knowledge in data– Levels at which biological information is used:
•comparing sequences – predict function of a newly discovered gene
•breaking down known 3D protein structures into bits to find patterns that can help predict how the protein folds
•modeling how proteins and metabolites in a cell work together to make the cell function…….
Informatics – Biologists’ Expectations
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Finally….What does informatics mean to biologists? The ultimate goal of analytical bioinformaticians is to develop predictive methods that allow biomedical researchers and scientists to model the function and phenotype of an organism based only on its genomic sequence. This is a grand goal, and one that will be approached only in small steps, by many scientists from different but allied disciplines working cohesively.
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Biology – Data StructuresFour broad categories:1.Strings: To represent DNA, RNA, amino
acid sequences of proteins 2.Trees: To represent the evolution of
various organisms (Taxonomy) or structured knowledge (Ontologies)
3.Sets of 3D points and their linkages: To represent protein structures
4.Graphs: To represent metabolic, regulatory, and signaling networks or pathways
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Biology – Data StructuresBiologists are also interested in1.Substrings2.Subtrees3.Subsets of points and linkages, and 4.Subgraphs. Beware: Biological data is often characterized by huge size, the presence of laboratory errors (noise), duplication, and sometimes unreliability.
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Support Complex Queries – A typical demand• Get me all genes involved in or associated with
brain development that are differentially expressed in the Central Nervous System.
• Get me all genes involved in brain development in human and mouse that also show iron ion binding activity.
• For this set of genes, what aspects of function and/or cellular localization do they share?
• For this set of genes, what mutations are reported to cause pathological conditions?
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Model Organism Databases: Common Issues
• Heterogeneous Data Sets - Data Integration– From Genotype to Phenotype– Experimental and Consensus Views
• Incorporation of Large Datasets– Whole genome annotation pipelines– Large scale mutagenesis/variation projects
(dbSNP)• Computational vs. Literature-based Data
Collection and Evaluation (MedLine)• Data Mining
– extraction of new knowledge– testable hypotheses (Hypothesis Generation)
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Bioinformatic Data-1978 to present• DNA sequence• Gene expression• Protein expression• Protein Structure• Genome mapping• SNPs & Mutations
• Metabolic networks• Regulatory networks• Trait mapping• Gene function
analysis• Scientific literature• and others………..
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Human Genome Project – Data Deluge Database name Records
Nucleotide 12,427,463Protein 419,759Structure 11,232Genome Sequences 75
Popset 21,010SNP 11,751,2163D Domains 41,857Domains 19
GEO Datasets 5,036
GEO Expressions 16,246,778
UniGene 123,777
UniSTS 323,773
PubMed Central 4,278
HomoloGene 19,520
Taxonomy 1
No. of Human Gene Records currently in NCBI: 29413 (excluding pseudogenes, mitochondrial genes and obsolete records).Includes ~460 microRNAs
NCBI Human Genome Statistics – as on February12, 2008
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The Gene Expression Data DelugeTill 2000: 413 papers on microarray!
Year PubMed Articles
2001 8342002 15572003 24212004 35082005 44002006 48242007 51082008 5023…
Problems Deluge!Allison DB, Cui X, Page GP, Sabripour M. 2006. Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet. 7(1): 55-65.
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• 3 scientific journals in 1750• Now - >120,000 scientific journals!• >600,000 medical articles/year• >4,000,000 scientific articles/year• >18 million abstracts in PubMed
derived from >32,500 journals
Information Deluge…..
A researcher would have to scan 130 different journals and read 27 papers per day to follow a single disease, such as breast cancer (Baasiri et al., 1999 Oncogene 18: 7958-7965).
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•Accelerin•Antiquitin•Bang Senseless•Bride of Sevenless•Christmas Factor•Cockeye•Crack•Draculin•Dickie’s small eye
Disease names• Mobius Syndrome with
Poland’s Anomaly• Werner’s syndrome• Down’s syndrome• Angelman’s syndrome• Creutzfeld-Jacob
disease
•Draculin•Fidgetin•Gleeful•Knobhead•Lunatic Fringe•Mortalin•Orphanin•Profilactin•Sonic Hedgehog
Data-driven Problems…..
Gene Nomenclature
• How to name or describe proteins, genes, drugs, diseases and conditions consistently and coherently?
• How to ascribe and name a function, process or location consistently?• How to describe interactions, partners, reactions and complexes?
• Develop/Use controlled or restricted vocabularies (IUPAC-like naming conventions, HGNC, MGI, UMLS, etc.)
• Create/Use thesauruses, central repositories or synonym lists (MeSH, UMLS, etc.)
• Work towards synoptic reporting and structured abstracting
Some Solutions
1. Generally, the names refer to some feature of the mutant phenotype
2. Dickie’s small eye (Thieler et al., 1978, Anat Embryol (Berl), 155: 81-86) is now Pax6
3. Gleeful: "This gene encodes a C2H2 zinc finger transcription factor with high sequence similarity to vertebrate Gli proteins, so we have named the gene gleeful (Gfl)." (Furlong et al., 2001, Science 293: 1632)
What’s in a name!Rose is a rose is a rose is a rose!
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Rose is a rose is a rose is a rose….. Not Really!
Image Sources: Somewhere from the internet…
What is a cell?• any small compartment• (biology) the basic structural and functional unit of all
organisms; they may exist as independent units of life (as in monads) or may form colonies or tissues as in higher plants and animals
• a device that delivers an electric current as a result of chemical reaction
• a small unit serving as part of or as the nucleus of a larger political movement
• cellular telephone: a hand-held mobile radiotelephone for use in an area divided into small sections, each with its own short-range transmitter/receiver
• small room in which a monk or nun lives• a room where a prisoner is kept
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Foundation Model Explorer
Semantic Groups, Types and Concepts:
• Semantic Group Biology – Semantic Type Cell
• Semantic Groups Object OR Devices – Semantic Types Manufactured Device or Electrical Device or Communication Device
• Semantic Group Organization – Semantic Type Political Group
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Database name
No. of Records
Query= p53
Query= TP53
(HGNC)
Query= p53 OR TP53
PubMed 46,838 3041 47,566PMC 16,490 1037 16,750Book 782 504 820Nucleotide 9473 592 9773Protein 6219 509 6377Genome 22 1 23OMIM 403 141 414SNP 424 337 453Gene 1642 338 1750Homologene 63 9 68GEO Profiles 352,684 15,140 358,999Cancer Chr 302 161 463
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Hepatocellular Carcinoma
CTNNB1MET
TP53
1. COLORECTAL CANCER [3-BP DEL, SER45DEL]2. COLORECTAL CANCER [SER33TYR]3. PILOMATRICOMA, SOMATIC [SER33TYR]4. HEPATOBLASTOMA, SOMATIC [THR41ALA]5. DESMOID TUMOR, SOMATIC [THR41ALA]6. PILOMATRICOMA, SOMATIC [ASP32GLY]7. OVARIAN CARCINOMA, ENDOMETRIOID TYPE, SOMATIC [SER37CYS]8. HEPATOCELLULAR CARCINOMA SOMATIC [SER45PHE]9. HEPATOCELLULAR CARCINOMA SOMATIC [SER45PRO]10. MEDULLOBLASTOMA, SOMATIC [SER33PHE]
1. HEPATOCELLULAR CARCINOMA SOMATIC [ARG249SER]
TP53*
aflatoxin B1, a mycotoxin induces a very specific G-to-T mutation at codon 249 in the tumor suppressor gene p53.
Environmental Effects
Many disease states are complex, because of many genes (alleles & ethnicity, gene families, etc.), environmental effects (life style, exposure, etc.) and the interactions.
The REAL Problems
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HEPATOCELLULAR CARCINOMALIVER:
•Hepatocellular carcinoma; •Micronodular cirrhosis; •Subacute progressive viral hepatitis
NEOPLASIA: •Primary liver cancer
CTNNB1MET
TP53
1. ALK in cardiac myocytes 2. Cell to Cell Adhesion Signaling 3. Inactivation of Gsk3 by AKT causes
accumulation of b-catenin in Alveolar Macrophages
4. Multi-step Regulation of Transcription by Pitx2 5. Presenilin action in Notch and Wnt signaling 6. Trefoil Factors Initiate Mucosal Healing 7. WNT Signaling Pathway
1. CBL mediated ligand-induced downregulation of EGF receptors
2. Signaling of Hepatocyte Growth Factor Receptor 1. Estrogen-responsive protein Efp
controls cell cycle and breast tumors growth
2. ATM Signaling Pathway 3. BTG family proteins and cell
cycle regulation 4. Cell Cycle 5. RB Tumor
Suppressor/Checkpoint Signaling in response to DNA damage
6. Regulation of transcriptional activity by PML
7. Regulation of cell cycle progression by Plk3
8. Hypoxia and p53 in the Cardiovascular system
9. p53 Signaling Pathway 10. Apoptotic Signaling in Response
to DNA Damage 11. Role of BRCA1, BRCA2 and ATR
in Cancer Susceptibility….Many More…..
The REAL Problems
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Integrative Genomics - what is it?Another buzzword or a meaningful concept useful for
biomedical research?Acquisition, Integration, Curation, and
Analysis of biological data
Integrative Genomics: the study of complex interactions between genes, organism and environment, the triple helix of biology. Gene <–> Organism <-> Environment It is definitely beyond the buzzword stage - Universities now have programs named 'Integrated Genomics.'
Hypothesis
Information is not knowledge - Albert Einstein
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1. Link driven federations• Explicit links between databanks.
2. Warehousing• Data is downloaded, filtered,
integrated and stored in a warehouse. Answers to queries are taken from the warehouse.
3. Others….. Semantic Web, etc………
Methods for Integration
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1. Creates explicit links between databanks
2. query: get interesting results and use web links to reach related data in other databanks
Examples: NCBI-Entrez, SRS
Link-driven Federations
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http://www.ncbi.nlm.nih.gov/Database/datamodel/
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http://www.ncbi.nlm.nih.gov/Database/datamodel/
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http://www.ncbi.nlm.nih.gov/Database/datamodel/
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http://www.ncbi.nlm.nih.gov/Database/datamodel/
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http://www.ncbi.nlm.nih.gov/Database/datamodel/
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1.Advantages• complex queries• Fast
2.Disadvantages• require good knowledge• syntax based• terminology problem not solved
Link-driven Federations
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Data is downloaded, filtered, integrated and stored in a warehouse. Answers to queries are taken from the warehouse.
Data Warehousing
Advantages1. Good for very-specific,
task-based queries and studies.
2. Since it is custom-built and usually expert-curated, relatively less error-prone.
Disadvantages1. Can become quickly
outdated – needs constant updates.
2. Limited functionality – For e.g., one disease-based or one system-based.
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GATACA – Genetic Associations to Anatomy and Clinical Abnormalities (http://gataca.cchmc.org)
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1. Finding similarities among strings2. Detecting certain patterns within
strings3. Finding similarities among parts of
spatial structures (e.g. motifs)4. Constructing trees
• Phylogenetic or taxonomic trees: evolution of an organism
• Ontologies – structured/hierarchical representation of knowledge
5. Classifying new data according to previously clustered sets of annotated data
Algorithms in Bioinformatics
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6. Reasoning about microarray data and the corresponding behavior of pathways
7. Predictions of deleterious effects of changes in DNA sequences
8. Computational linguistics: NLP/Text-mining. Published literature or patient records
9. Graph Theory – Gene regulatory networks, functional networks, etc.
10.Visualization and GUIs (networks, application front ends, etc.)
Algorithms in Bioinformatics
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Disease Gene Identification and Prioritization
Hypothesis: Majority of genes that impact or cause disease share membership in any of several functional relationships OR Functionally similar or related genes cause similar phenotype.
Functional Similarity – Common/shared•Gene Ontology term•Pathway•Phenotype•Chromosomal location•Expression•Cis regulatory elements (Transcription factor binding sites)•miRNA regulators•Interactions•Other features…..
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1. Most of the common diseases are multi-factorial and modified by genetically and mechanistically complex polygenic interactions and environmental factors.
2. High-throughput genome-wide studies like linkage analysis and gene expression profiling, tend to be most useful for classification and characterization but do not provide sufficient information to identify or prioritize specific disease causal genes.
Background, Problems & Issues
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3. Since multiple genes are associated with same or similar disease phenotypes, it is reasonable to expect the underlying genes to be functionally related.
4. Such functional relatedness (common pathway, interaction, biological process, etc.) can be exploited to aid in the finding of novel disease genes. For e.g., genetically heterogeneous hereditary diseases such as Hermansky-Pudlak syndrome and Fanconi anaemia have been shown to be caused by mutations in different interacting proteins.
Background, Problems & Issues
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1. Direct protein–protein interactions (PPI) are one of the strongest manifestations of a functional relation between genes.
2. Hypothesis: Interacting proteins lead to same or similar disease phenotypes when mutated.
3. Several genetically heterogeneous hereditary diseases are shown to be caused by mutations in different interacting proteins. For e.g. Hermansky-Pudlak syndrome and Fanconi anaemia. Hence, protein–protein interactions might in principle be used to identify potentially interesting disease gene candidates.
PPI - Predicting Disease Genes
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Known Disease Genes
Direct Interactants of Disease Genes
Mining human interactome
HPRDBioGrid
Which of these interactants are potential new candidates?
Indirect Interactants of Disease Genes
7
66
778
Prioritize candidate genes in the interacting partners of the disease-related genes•Training sets: disease related genes •Test sets: interacting partners of the training genes
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Example: Breast cancer OMIM genes (level 0)
Directly interacting genes (level 1)
Indirectly interacting genes (level2)
15 342 2469!
15 342 2469
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ToppGene – General Schemahttp://
toppgene.cchmc.org
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TOPPGene - Data Sources1. Gene Ontology: GO and NCBI Entrez Gene2. Mouse Phenotype: MGI (used for the first
time for human disease gene prioritization)3. Pathways: KEGG, BioCarta, BioCyc,
Reactome, GenMAPP, MSigDB4. Domains: UniProt (Pfam, Interpro,etc.)5. Interactions: NCBI Entrez Gene (Biogrid,
Reactome, BIND, HPRD, etc.)6. Pubmed IDs: NCBI Entrez Gene7. Expression: GEO8. Cytoband: MSigDB9. Cis-Elements: MSigDB10.miRNA Targets: MSigDB
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PubMed
Medical Informatics
Patient Records
Disease Database→Name→Synonyms→Related/Similar Diseases→Subtypes→Etiology →Predisposing Causes→Pathogenesis→Molecular Basis→Population Genetics→Clinical findings→System(s) involved→Lesions →Diagnosis→Prognosis→Treatment→Clinical Trials……
Clinical Trials
Bioinformatics
Genome
Transcriptome
Proteome
Interactome
Metabolome
Physiome
Regulome Variome
Pathome
Phar
mac
ogen
ome
Disease
World
OMIM
►Personalized Medicine►Decision Support System►Outcome Predictor►Course Predictor►Diagnostic Test Selector►Clinical Trials Design►Hypothesis Generator…..
the Ultimate Goal…….
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