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 metapath 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. INDEX TERMS Drug-disease association, drug repositioning, ensemble learning, gene ontology profile, meta-path, tripartite network.

Multi-Level Biological Network Analysis and Drug Repurposing Based on Leukocyte Transcriptomics in Severe COVID-19: In Silico Systems Biology to Precision Medicine

“…With the use of network analysis, central node identification using various centrality measurements and community detection by several network clustering algorithms [ 46 , 47 ] have been widely used in much research. These approaches were successfully applied in several applications to identify key disease-related genes, disease–disease associations, disease–protein associations, and drug–disease associations [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. Additionally, the benefit of the network analysis is drug repositioning or drug repurposing, characterized by discovering a new role of treatment from existing drugs based on the key disease-related genes identified from the biological network [ 58 ].…”

Gene based message passing for drug repurposing

“…Kawichai et al. 10 constructed a network based on disease, drug and gene ontology information, and designed meta-path to calculate representations of drug-disease pairs. Zhou et al.…”

Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction

“…Network-based methods have been successfully used for predicting several tasks, including disease-disease association predictions, 18 disease protein association predictions, [19][20][21][22][23][24][25] and drug-disease association predictions. 26,27 Several computational drug repositioning approaches focus on a heterogeneous network of different types of nodes such as drugs, proteins, and diseases. Wu et al proposed the ensemble meta-paths and singular value decomposition (EMP-SVD) model, which generates five meta-paths, and constructed the latent features of drugs and diseases using the singular value decomposition (SVD) technique.…”

“…Another technique that utilized the function of proteins or gene ontology (GO) profiles as the middle nodes to link drugs and diseases in the tripartite network, called meta-path-based gene ontology profiles for predicting drug-disease associations (MGP-DDA), was proposed. 26 The MGP-DDA model integrates a meta-path based on GO terms to construct a drug repositioning model.…”