Graph Convolutional Autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions

The computational prediction of novel drug-target interactions (DTIs) may effectively speed up the process of drug repositioning and reduce its costs. Most previous methods integrated multiple kinds of connections about drugs and targets by constructing shallow prediction models. These methods faile...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE/ACM transactions on computational biology and bioinformatics Vol. 19; no. 1; pp. 455 - 464
Main Authors:	Sun, Chang, Xuan, Ping, Zhang, Tiangang, Ye, Yilin
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adversarial regularization Classifiers Computational modeling Computer applications Computer networks Diffusion tensor imaging Drug interaction Drug Interactions Drug Repositioning drug-target interaction Drugs generative adversarial network Generative adversarial networks graph convolutional autoencoder Heterogeneous networks LightGBM Neural Networks, Computer Nodes Normal distribution Pharmaceutical Preparations Prediction models Predictions Predictive models Proteins Therapeutic targets
ISSN:	1545-5963, 1557-9964, 1557-9964
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!