Modeling drug mechanism of action with large scale gene-expression profiles using GPAR, an artificial intelligence platform.

Journal: BMC bioinformatics
PMID: 33413089

Abstract

BACKGROUND: Querying drug-induced gene expression profiles with machine learning method is an effective way for revealing drug mechanism of actions (MOAs), which is strongly supported by the growth of large scale and high-throughput gene expression databases. However, due to the lack of code-free and user friendly applications, it is not easy for biologists and pharmacologists to model MOAs with state-of-art deep learning approach.

Authors

  • Shengqiao Gao
    Beijing Institute of Pharmacology and Toxicology, State Key Laboratory of Toxicology and Medical Countermeasures, Beijing, 100850, China.
  • Lu Han
    Pfizer Worldwide Chemical Research and Development, Pfizer Inc. Groton Connecticut 06340 USA Sebastien.Monfette@pfizer.com.
  • Dan Luo
    Shimadzu (China) Co., Ltd, Wuhan 430022, China.
  • Gang Liu
    Department of Interventional Radiology, Qinghai Red Cross Hospital, Xining, Qinghai, China.
  • Zhiyong Xiao
    School of Software, Jiangxi Agricultural University, Nanchang 330045, China.
  • Guangcun Shan
    School of Instrumentation Science and Opto-electronics Engineering, Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Beihang University, 100191 Beijing, China.
  • Yongxiang Zhang
    Beijing Institute of Pharmacology and Toxicology, State Key Laboratory of Toxicology and Medical Countermeasures, Beijing, 100850, China. zhangyx@bmi.ac.cn.
  • Wenxia Zhou
    Beijing Institute of Pharmacology and Toxicology, State Key Laboratory of Toxicology and Medical Countermeasures, Beijing, 100850, China. zhouwx@bmi.ac.cn.

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