GPDRP: a multimodal framework for drug response prediction with graph transformer.

ABSTRACT:

Background

In the field of computational personalized medicine, drug response prediction (DRP) is a critical issue. However, existing studies often characterize drugs as strings, a representation that does not align with the natural description of molecules. Additionally, they ignore gene pathway-specific combinatorial implication.

Results

In this study, we propose drug Graph and gene Pathway based Drug response prediction method (GPDRP), a new multimodal deep learning model for predicting drug responses based on drug molecular graphs and gene pathway activity. In GPDRP, drugs are represented by molecular graphs, while cell lines are described by gene pathway activity scores. The model separately learns these two types of data using Graph Neural Networks (GNN) with Graph Tra

SUBMITTER: Yang Y 

PROVIDER: S-EPMC10726525 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature