Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction.

Journal: BMC bioinformatics
PMID: 33183223

Abstract

BACKGROUND: Protein kinases are a large family of druggable proteins that are genomically and proteomically altered in many human cancers. Kinase-targeted drugs are emerging as promising avenues for personalized medicine because of the differential response shown by altered kinases to drug treatment in patients and cell-based assays. However, an incomplete understanding of the relationships connecting genome, proteome and drug sensitivity profiles present a major bottleneck in targeting kinases for personalized medicine.

Authors

  • Liang-Chin Huang
    School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Wayland Yeung
    Institute of Bioinformatics, University of Georgia, 120 Green St., Athens, GA, 30602, USA.
  • Ye Wang
    College of Computer Science and Technology, Jilin University, Changchun 130012, China.
  • Huimin Cheng
    Department of Statistics, University of Georgia, 310 Herty Drive, Athens, GA, 30602, USA.
  • Aarya Venkat
    Department of Biochemistry and Molecular Biology, 120 Green St., Athens, GA, 30602, USA.
  • Sheng Li
    School of Data Science, University of Virginia, Charlottesville, VA, United States.
  • Ping Ma
    Department of Statistics, University of Georgia, Athens, GA 30602, USA.
  • Khaled Rasheed
    Department of Computer Science, University of Georgia, Athens, GA, 30602, USA.
  • Natarajan Kannan
    Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA. nkannan@uga.edu.

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