December 2024 · Cancer Research Communications · First author

DeePathNet: a transformer-based deep learning model integrating multi-omic data with cancer pathways

Zhaoxiang Cai, Rebecca C Poulos, Jianmin Liu, Qing Zhong

Abstract

DeePathNet is a transformer-based deep learning model that integrates multi-omic data with biological pathway information. By embedding pathway knowledge directly into the model architecture, DeePathNet improves the interpretability and performance of cancer subtype classification and drug response prediction. We demonstrate the utility of DeePathNet on large-scale datasets, highlighting its ability to identify pathway-level biomarkers and mechanisms of action.

BibTeX

@article{cai2024cai2024deepathnet,
  title = {{DeePathNet: a transformer-based deep learning model integrating multi-omic data with cancer pathways}},
  author = {Zhaoxiang Cai and Rebecca C Poulos and Jianmin Liu and Qing Zhong},
  journal = {Cancer Research Communications},
  year = {2024},
  doi = {10.1158/2767-9764.CRC-24-0285}
}