ScXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning.

ABSTRACT: Drug response prediction at the single-cell level provides guidance for drug development and treatment. Existing methods typically rely on integrating data from bulk RNA sequencing and single-cell RNA sequencing, transferring overall cell line drug response labels to individual cells. However, the inherent differences between these data types and the assumption that the cell line response represents each cell's response can affect prediction accuracy. This study proposes a heterogeneous network transfer learning model, scXDR, for drug response prediction across single-cell datasets. The model uses heterogeneous networks to integrate features and associations among drugs, genes, and cells, performing message passing, feature and structure alignment, structure reconstruction, and drug-cell s

SUBMITTER: Qi G 

PROVIDER: S-EPMC12859067 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature