A drug repositioning algorithm based on a deep autoencoder and adaptive fusion.

ABSTRACT:

Background

Drug repositioning has caught the attention of scholars at home and abroad due to its effective reduction of the development cost and time of new drugs. However, existing drug repositioning methods that are based on computational analysis are limited by sparse data and classic fusion methods; thus, we use autoencoders and adaptive fusion methods to calculate drug repositioning.

Results

In this study, a drug repositioning algorithm based on a deep autoencoder and adaptive fusion was proposed to mitigate the problems of decreased precision and low-efficiency multisource data fusion caused by data sparseness. Specifically, a drug is repositioned by fusing drug-disease associations, drug target proteins, drug chemical structures and drug side effects. First, drug feat

SUBMITTER: Chen P 

PROVIDER: S-EPMC8556784 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature