Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data.

ABSTRACT: Drug screening data from massive bulk gene expression databases can be analyzed to determine the optimal clinical application of cancer drugs. The growing amount of single-cell RNA sequencing (scRNA-seq) data also provides insights into improving therapeutic effectiveness by helping to study the heterogeneity of drug responses for cancer cell subpopulations. Developing computational approaches to predict and interpret cancer drug response in single-cell data collected from clinical samples can be very useful. We propose scDEAL, a deep transfer learning framework for cancer drug response prediction at the single-cell level by integrating large-scale bulk cell-line data. The highlight in scDEAL involves harmonizing drug-related bulk RNA-seq data with scRNA-seq data and transferring the model

SUBMITTER: Chen J 

PROVIDER: S-EPMC9618578 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature