Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes.

ABSTRACT:

Background

Drug-drug interaction (DDI) is a serious public health issue. The L1000 database of the LINCS project has collected millions of genome-wide expressions induced by 20,000 small molecular compounds on 72 cell lines. Whether this unified and comprehensive transcriptome data resource can be used to build a better DDI prediction model is still unclear. Therefore, we developed and validated a novel deep learning model for predicting DDI using 89,970 known DDIs extracted from the DrugBank database (version 5.1.4).

Results

The proposed model consists of a graph convolutional autoencoder network (GCAN) for embedding drug-induced transcriptome data from the L1000 database of the LINCS project; and a long short-term memory (LSTM) for DDI prediction. Comparative evaluation of

SUBMITTER: Luo Q 

PROVIDER: S-EPMC8194123 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature