DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network representation.

ABSTRACT:

Background

Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid.

Methods

A novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI, was generated. The proposed approach simultaneously fused the topology of complex networks and diverse information from heterogeneous data sources, and coped with the noisy, incomplete, and high-dimensi

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC7666529 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature