Exploiting common patterns in diverse cancer types via multi-task learning.

ABSTRACT: Cancer prognosis requires precision to identify high-risk patients and improve survival outcomes. Conventional methods struggle with the complexity of genetic biomarkers and diverse medical data. Our study uses deep learning to distil high-dimensional medical data into low-dimensional feature vectors exploring shared patterns across cancer types. We developed a multi-task bimodal neural network integrating RNA Sequencing and clinical data from three The Cancer Genome Atlas project datasets: Breast Invasive Carcinoma, Lung Adenocarcinoma, and Colon Adenocarcinoma. Our approach significantly improved prognosis prediction, especially for Colon Adenocarcinoma, with up to 26% increase in concordance index and 41% in the area under the precision-recall curve. External validation with Small Cell

SUBMITTER: Wu BR 

PROVIDER: S-EPMC11522563 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature