Personalized_Medicine_A_New_Medical_and_Social_Challenge

3.1.9 Ontology Data

Ontology is the structural framework for representing knowledge as a hierarchy of
terms and their interrelationships. Two widely used ontologies in biomedical
research are Gene Ontology (GO), which unifies the knowledge about functioning
of genes and gene products,^85 and Disease Ontology (DO), which unifies the
knowledge about relationships between diseases.^86 Ontology data have been useful
in complementing genomic data in data integration approaches. Gene Ontology
(GO) provides a large set of terms describing functions of genes and proteins. These
terms are arranged in a hierarchically structured vocabulary that includes three
domains: cellular component (CC), molecular function (MF), and biological pro-
cess (BP).^87 To effectively incorporate GO data into an integration framework with
other data sets, we use the following representations:

Table 1 Databases related to human diseases, genes, proteins, and drugs

BioGRID http://thebiogrid.org/ PPI and genetic

STRING http://string-db.org/ interaction

HPRD http://www.hprd.org/

KEGG http://www.genome.jp/kegg/ Metabolic pathways

GEO http://www.ncbi.nlm.nih.gov/geo/ Gene expression

ArrayExpress http://www.ebi.ac.uk/arrayexpress/

SMD http://smd.princeton.edu/

TRANSPATH http://genexplain.com/transpath‐ 1 Cell signaling data

OMIM http://www.ncbi.nlm.nih.gov/omim Gene-disease

CTD http://ctdbase.org/ associations

FunDO http://django.nubic.northwestern.edu/

fundo/

DrugBank http://www.drugbank.ca/ Chemical-target

KEGG BRITE http://www.genome.jp/kegg/brite.html interactions

SuperTarget http://bioinf-apache.charite.de/

supertarget_v2/

BRENDA http://www.brenda-enzymes.org/

KEGG LIGAND http://www.genome.jp/kegg/ligand.html Chemical structure of

drugs

SIDER http://sideeffects.embl.de/ Drug side effects

CMAP (special to

drugs)

http://www.broadinstitute.org/cmap/ Gene expression

These databases are used in data integration studies aimed atprotein function prediction, drug
repurposing, disease classification, disease association prediction and prioritization of disease
genes
aSee Sect.3.1for details

(^85) Ashburner et al. ( 2000 ).
(^86) Schriml et al. ( 2012 ).
(^87) Ashburner et al. ( 2000 ).
Computational Methods for Integration of Biological Data 153