Oct 29, 2018

Drug-target interaction prediction using Doubly Graph Regularized Matrix Completion

BioRxiv : the Preprint Server for Biology
Aanchal Mongia, Angshul Majumdar


Motivation: The identification of interactions between drugs and target proteins is crucial in pharmaceutical sciences. The experimental validation of interactions in genomic drug discovery is laborious and expensive; hence, there is a need of efficient and accurate in silico techniques which can predict potential drug-target interactions to narrow down the search space for experimental verification. Results: In this work, we propose a new framework, namely, Doubly Graph Regularized Matrix Completion, which predicts the interactions between drugs and proteins from three inputs: known drug-target network, similarities over drugs and those over targets. The proposed method focuses on finding a low rank interaction matrix that is structured by the proximities of drugs and targets encoded by graphs. Extensive cross validation experiments on four benchmark datasets using standard evaluation metrics (AUPR and AUC)` show that the proposed algorithm improves the predictive performance and outperforms recent state-of-the-art computational methods. Availability and Implementation: https://github.com/aanchalMongia/DGRMCforDTI

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Mentioned in this Paper

Cross Validation
Anatomical Space Structure
Drug Industry
Pharmacologic Substance
Drug Interactions

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