Role of Bioinformatics in Integrating Various Data and Biomarker Discovery

There is a need for integration of high-throughput biological data with less structured clinical data for discovery of biomarkers. A data integration strategy has been described that implements a clinical and biological database and a wiki interface (Sorani et al. 2010). The authors integrated parameters across clinical trials and associated genetic, gene expression and protein data to explore disease heterogeneity and develop predictive biomarkers. They undertook extensive clinical data standardization and biological data summarization to support biomarker discovery in rheumatoid arthritis. According to the authors of this study, a key enabler for future integration efforts will be the prospective adoption of standard clinical trial data nomenclature using a controlled vocabulary and ontology. Such standardization could facilitate future data loading, integration and cross-trial analysis and, ultimately, biomarker and drug discovery efforts.

Ariana Pharma’s KEM® (knowledge extraction and management) rules-based data mining analytics software can be applied to identify predictive biomarkers of clinical adverse events, safety and efficacy. It can also identify biomarkers for potential applications in prognostic and companion diagnostic assays.

< Prev   CONTENTS   Source   Next >