Investigating Multiview and Multitask Learning Frameworks for Predicting Drug-Disease Associations
Sai Nivedita Chandrasekaran, Alexios Koutsoukas, Jun Huan · 2016
Drugs exhibit their therapeutic effects by interacting and modulating one or multiple protein targets simultaneously; hence deeper insights of complex drug-disease-targets associations is of paramount importance in drug discovery. During the last decades the idea of drug re-purposing has been explored, where an old drug is utilized to treat a new disease. Unveiling potentially new and interesting drug-disease-target associations is a challenging task, considering the complexity of biological systems. Hence novel computational approaches are of high demand to analyze, disseminate and predict new interesting interactions, utilizing the growing body of data from different domains, which could then act as starting point for therapy development. Recently, the idea of data integration has been gaining momentum, where information from heterogeneous sources is combined with the expectation to provide additional information regarding the underlying links between drugs-diseases-targets, that otherwise would be difficult to study. In this study, we investigate a new direction for studying drug-disease associations by utilizing and combining multiview and mutlitask learning through integration of heterogeneous source of data. Our results show the advantages of exploring these methods to more effectively combine information from varied sources. We use multiple data sets to show the consistency of the results obtained.