A drug response prediction method for single-cell tumors combining attention networks and transfer learning.

ABSTRACT:

Introduction

Accurately predicting tumor cell line responses to therapeutic drugs is essential for personalized cancer treatment. Current methods using bulk cell data fail to fully capture tumor heterogeneity and the complex mechanisms underlying treatment responses.

Methods

This study introduces a novel method, ATSDP-NET (Attention-based Transfer Learning for Enhanced Single-cell Drug Response Prediction), which combines bulk and single-cell data. The model utilizes transfer learning and attention networks to predict drug responses in single-cell tumor data, after pre-training on bulk cell gene expression data. A multi-head attention mechanism is incorporated to enhance the model's expressive power and prediction accuracy by identifying gene expression patterns linked to dr

SUBMITTER: Zhou B 

PROVIDER: S-EPMC12408664 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature