Beyond data integration

Abstract

Pharmaceutical R&D organizations have no shortage of experimental data or annotation information. However, the sheer volume and complexity of this information results in a paralyzing inability to make effective use of it for predicting drug efficacy and safety. Data integration efforts are legion, but even in the rare instances where they succeed, they are found to be insufficient to advance programs because interpretation of query results becomes a research project in itself. In this review, we propose a coherent, interoperable platform comprising knowledge engineering and hypothesis generation components for rapidly making determinations of confidence in mechanism and safety (among other goals) using experimental data and expert knowledge.