ScATD: a high-throughput and interpretable framework for single-cell cancer drug resistance prediction and biomarker identification.

ABSTRACT: Transfer learning has been widely applied to drug sensitivity prediction based on single-cell RNA sequencing, leveraging knowledge from large datasets of cancer cell lines or other sources to improve the prediction of drug responses. However, previous studies require model fine-tuning for different patient single-cell datasets, limiting their ability to meet the clinical need for high-throughput rapid prediction. In this research, we introduce single-cell Adaptive Transfer and Distillation model (scATD), a transfer learning framework leveraging large language models for high-throughput drug sensitivity prediction. Based on different large language models (scFoundation and Geneformer) and transfer strategies, scATD includes three distinct sub-models: scATD-sf, scATD-gf, and scATD-sf-dist. s

SUBMITTER: Zhou M 

PROVIDER: S-EPMC12159290 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature