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 biom

SUBMITTER: Cai Z 

PROVIDER: S-EPMC11652962 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature