Weighted multiview learning for predicting drug-disease associations

The paradigm of drug discovery has moved from finding new drugs that exhibit therapeutic properties for a disease to reusing existing approved drugs for a newer disease. The association between a drug and a disease involves a complex network of targets and pathways. In order to provide new insights, there has been a constant need for sophisticated tools that have the potential to discover new associations from the underlying drugs-disease interactions. In addition to computational tools, there has been an explosion of data available in terms of drugs, disease and their activity profiles. On one hand, researchers have been using existing machine learning tools that have shown great promise in predicting associations but on the other hand there has been a void in exploiting advance machine learning frameworks to handle this kind of data integration. In this paper, we propose a learning framework called weighted multi-view learning that is a variant of the Multi-view learning framework in which the views are assumed to contribute equally to the prediction whereas our method learns a weight for each view since we hypothesize that certain views might have better prediction capability than others.