Practice and Challenges of Building a Semantic Framework for Chemogenomics Research

Effective discovery of new drugs for complex diseases demands an integrative analysis of big data aggregated from diverse sources in chemical and biological domains, to help better understand the mechanism of drug actions and to quickly translate discovery to clinical applications. Conventional approaches are confronting critical challenges in the integration of those huge heterogeneous datasets and the rapid transformation from data to knowledge. Semantic technologies aimed at facilitating the building of a common framework that allows data sharing and utilization across applications and domains in the web, have been developed quickly and have been exhibiting a broad impact in life science. Chemogenomics serves as a bridge to connect various chemical and biological data, thus building a semantic framework for chemogenomics research could not only facilitate the development of this field but also advance the intersection among other domains. During the last few years, such framework has been developed and applied in addressing real problems. In the review, we will describe the major techniques needed to build a semantic framework, and will discuss the challenges of having such framework making a broader impact.