Meta-Path Based Gene Ontology Profiles for Predicting Drug-Disease Associations

Abstract

Drug repositioning, discovering new indications for existing drugs, is known to solve the bottleneck of drug discovery and development. To support a task of drug repositioning, many in silico methods have been proposed for predicting drug-disease associations. A meta-path based approach, which extracts network-based information through paths from a drug to a disease, can produce comparable performance with less required information when compared to other approaches. However, existing meta-path based methods typically use counts of extracted paths and discard information of intermediate nodes in those paths although they are very important indicators, such as drug-and disease-associated proteins. Herein, we propose an ensemble learning method with Meta-path based Gene ontology Profiles for predicting Drug-Disease Associations (MGP-DDA). We exploit gene ontology (GO) terms to link drugs and diseases to their associated functions and act as intermediate nodes in a drug-GO-disease tripartite network. For each drug-disease pair, MGP-DDA utilizes meta-paths to generate novel profiles of GO functions, termed as meta-path based GO profiles. We train bagging and boosting classifiers with those novel features to recognize known (positive) from unknown (unlabeled) drug-disease associations. Consequently, MGP-DDA outperforms the state-of-the-art methods and yields the precision of 88.6%. By MGP-DDA, the eminent number of new drug-disease associations with supporting evidence in ClinicalTrials.gov (37.7%) ensures the practicality of our method in drug repositioning.