Deep learning and multi-omics approach to predict drug responses in cancer.

Journal: BMC bioinformatics
PMID: 36443676

Abstract

BACKGROUND: Cancers are genetically heterogeneous, so anticancer drugs show varying degrees of effectiveness on patients due to their differing genetic profiles. Knowing patient's responses to numerous cancer drugs are needed for personalized treatment for cancer. By using molecular profiles of cancer cell lines available from Cancer Cell Line Encyclopedia (CCLE) and anticancer drug responses available in the Genomics of Drug Sensitivity in Cancer (GDSC), we will build computational models to predict anticancer drug responses from molecular features.

Authors

  • Conghao Wang
    School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
  • Xintong Lye
    School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
  • Rama Kaalia
    School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.
  • Parvin Kumar
    School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
  • Jagath C Rajapakse
    Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore; Singapore-MIT Alliance, Singapore; Department of Biological Engineering, Massachusetts Institute of Technology, USA.

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