Materials and Methods from DeePathNet: A Transformer-Based Deep Learning Model Integrating Multiomic Data with Cancer Pathways

Cai, Zhaoxiang; Poulos, Rebecca C.; Aref, Adel; Robinson, Phillip J.; Reddel, Roger R.; Zhong, Qing

doi:10.1158/2767-9764.28052280

Materials and Methods from DeePathNet: A Transformer-Based Deep Learning Model Integrating Multiomic Data with Cancer Pathways

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posted on 2024-12-18, 07:20 authored by Zhaoxiang Cai, Rebecca C. Poulos, Adel Aref, Phillip J. Robinson, Roger R. Reddel, Qing Zhong

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ARTICLE ABSTRACT

DeePathNet integrates cancer-specific biological pathways using transformer-based deep learning for enhanced cancer analysis. It outperforms existing models in predicting drug responses, cancer types, and subtypes. By enabling pathway-level biomarker discovery, DeePathNet represents a significant advancement in cancer research and could lead to more effective treatments.