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

Journal: BMC bioinformatics
Published Date: Jun 11, 2021

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).

Authors

  • Qichao Luo
    Big Data Decision Institute, Jinan University, Guangzhou, 510632, China.
  • Shenglong Mo
    Big Data Decision Institute, Jinan University, Guangzhou, 510632, China.
  • Yunfei Xue
    Big Data Decision Institute, Jinan University, Guangzhou, 510632, China.
  • Xiangzhou Zhang
    Big Data Decision Institute, Jinan University, Guangzhou, China.
  • Yuliang Gu
    Big Data Decision Institute, Jinan University, Guangzhou, 510632, China.
  • Lijuan Wu
    Big Data Decision Institute, Jinan University, Guangzhou, China.
  • Jia Zhang
    Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
  • Linyan Sun
    Xi'an Hospital of Traditional Chinese Medicine, Xi'an, 710021, China.
  • Mei Liu
    Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, Missouri, USA.
  • Yong Hu
    Big Data Decision Institute, Jinan University, Guangzhou, China.

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