VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder.

ABSTRACT:

Motivation

Accurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing. (i) Traditional methods do not consider the use of multi-source data and do not consider the complex relationship between data sources. (ii) How to better mine the hidden features of drug and target space from high-dimensional data, and better solve the accuracy and robustness of the model.

Results

To solve the above problems, a novel prediction model named VGAEDTI is proposed in this paper. We constructed a heterogeneous network with multiple sources of information using multiple types of drug and target dataIn order to obtain deeper features of drugs and targets, we use two different autoencoders. One is variational graph autoe

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10324201 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature