A generative deep neural network for pan-digestive tract cancer survival analysis.

ABSTRACT:

Background

The accurate identification of molecular subtypes in digestive tract cancer (DTC) is crucial for making informed treatment decisions and selecting potential biomarkers. With the rapid advancement of artificial intelligence, various machine learning algorithms have been successfully applied in this field. However, the complexity and high dimensionality of the data features may lead to overlapping and ambiguous subtypes during clustering.

Results

In this study, we propose GDEC, a multi-task generative deep neural network designed for precise digestive tract cancer subtyping. The network optimization process involves employing an integrated loss function consisting of two modules: the generative-adversarial module facilitates spatial data distribution understanding f

SUBMITTER: Xu L 

PROVIDER: S-EPMC11771125 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature