DeePathNet: A Transformer-Based Deep Learning Model Integrating Multiomic Data with Cancer Pathways.

Abstract

Abstract: Multiomic data analysis incorporating machine learning has the potential to significantly improve cancer diagnosis and prognosis. Traditional machine learning methods are usually limited to omic measurements, omitting existing domain knowledge, such as the biological networks that link molecular entities in various omic data types. Here, we develop a transformer-based explainable deep learning model, DeePathNet, which integrates cancer-specific pathway information into multiomic data analysis. Using a variety of big datasets, including ProCan-DepMapSanger, Cancer Cell Line Encyclopedia, and The Cancer Genome Atlas, we demonstrate and validate that DeePathNet outperforms traditional methods for predicting drug response and classifying cancer type and subtype. Combining biomedical knowledge and state-of-the-art deep learning methods, DeePathNet enables biomarker discovery at the pathway level, maximizing the power of data-driven approaches to cancer research. DeePathNet is available on GitHub at https://github.com/CMRI-ProCan/DeePathNet.

Significance: 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.