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.
Highlights
During increasing demands of effective drugs for various diseases, such as infectious diseases, cancers, and rare diseases, the traditional drug discovery and development process has been decreasingly attractive due to its time and cost consuming [1], [2]
We propose an ensemble learning method with Meta-path based Gene ontology Profiles for predicting
We considered only the positive drug-disease pairs because the existence of their associations has been already approved, and we expected that some gene ontology (GO) functions relevant to those pairs should be detected by the meta-paths
Summary
During increasing demands of effective drugs for various diseases, such as infectious diseases, cancers, and rare diseases, the traditional drug discovery and development process has been decreasingly attractive due to its time and cost consuming [1], [2]. To reduce the investment of this process, discovery of new indications for existing drugs, known as drug repositioning, has become an increasingly popular strategy. The first important step of drug repositioning is to identify a candidate drug and its new indication with high reliability [4]. To efficiently perform this task, several computational methods have been proposed with various strategies. With drug-drug similarities based on chemical structures and disease-disease similarities based on disease phenotypes, Wang et al constructed a three-layer heterogeneous network model
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