TransCDR: a deep learning model for enhancing the generalizability of drug activity prediction through transfer learning and multimodal data fusion.

ABSTRACT:

Background

Accurate and robust drug response prediction is of utmost importance in precision medicine. Although many models have been developed to utilize the representations of drugs and cancer cell lines for predicting cancer drug responses (CDR), their performances can be improved by addressing issues such as insufficient data modality, suboptimal fusion algorithms, and poor generalizability for novel drugs or cell lines.

Results

We introduce TransCDR, which uses transfer learning to learn drug representations and fuses multi-modality features of drugs and cell lines by a self-attention mechanism, to predict the IC₅₀ values or sensitive states of drugs on cell lines. We are the first to systematically evaluate the generalization of the CDR prediction model to n

SUBMITTER: Xia X 

PROVIDER: S-EPMC11462810 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature