The presented results in this work demonstrates that defining robust gene signatures for diseases and drugs from expression profiles and literature and using protein networks to refine and prioritize genes is valuable and have potential in clinical pharmacogenomics research.
This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics.
The lack of formal semantics for the data is a source of potential ambiguity that makes it difficult to create reliable information technology systems for enabling clinical pharmacogenomics.
However, many of the practical challenges of clinical pharmacogenomics are the same that have plagued prognostic and predictive marker research for decades.
The impact of non-genetic and environmental factors, epigenetic variations and circadian rhythms on an individual's response need to be assessed to make pharmacogenomics clinically indicated.
Clinical pharmacogenomics, aimed at integrating genomic information with clinical practices to facilitate the prediction of drug response, has recently emerged as a vital area of public health.
The implementation of GWASes by functional mapping leads to the birth of a dynamic model, fGWAS, for studying and characterizing clinical pharmacogenomics toward personalized medicine.
There have been schemes developed to correlate genotypes to phenotypes which are relevant for clinical pharmacogenomics.
The UF Health PMP IGNITE project builds on the existing framework for clinical pharmacogenomics already in place at UF Health, launched in 2011.
However, the primary knowledge needed for clinical pharmacogenomics is currently captured in either unstructured text or semi-structured formalisms.
