GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning.

ABSTRACT: The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and netw

SUBMITTER: Yao S 

PROVIDER: S-EPMC11275840 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature