Single-Cell Techniques and Deep Learning in Predicting Drug Response.

Journal: Trends in pharmacological sciences
PMID: 33153777

Abstract

Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single-cell techniques allow the response of a tumor to drug exposure to be more thoroughlyinvestigated. Deep learning (DL) models have successfully extracted features from complex bulk sequence data to predict drug responses. We review recent innovations in single-cell technologies and DL-based approaches related to drug sensitivity predictions. We believe that, by using insights from bulk sequencedata, deep transfer learning (DTL) can facilitate the use of single-cell data for training superior DL-based drug prediction models.

Authors

  • Zhenyu Wu
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
  • Patrick J Lawrence
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
  • Anjun Ma
    Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
  • Jian Zhu
  • Dong Xu
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
  • Qin Ma
    Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, GA 30602, USA BioEnergy Science Center, TN 37831, USA.

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