The exponential growth of experimental and clinical data generated from systematic
studies, the complexity in health and diseases, and the request for the establishment
of systems models are bringing bioinformatics to the center stage of pharmacogenomics
and systems biology. Bioinformatics plays an essential role in bridging the gap among
different knowledge domains for the translation of the voluminous data into better
diagnosis, prognosis, prevention, and treatment. Bioinformatics is essential in finding
the spatiotemporal patterns in pharmacogenomics, including the time-series analyses
of the associations between genetic structural variations and functional alterations
such as drug responses. The elucidation of the cross talks among different systems
levels and time scales can contribute to the discovery of accurate and robust biomarkers
at various diseases stages for the development of systems and dynamical medicine.
Various resources are available for such purposes, including databases and tools supporting
"omics" studies such as genomics, proteomics, epigenomics, transcriptomics, metabolomics,
lipidomics, pharmacogenomics, and chronomics. The combination of bioinformatics and
health informatics methods would provide powerful decision support in both scientific
and clinical environments. Data integration, data mining, and knowledge discovery
(KD) methods would enable the simulation of complex systems and dynamical networks
to establish predictive models for achieving predictive, preventive, and personalized
medicine.
