Improved supervised prediction of aging-related genes via weighted dynamic network analysis.

ABSTRACT:

Background

This study focuses on the task of supervised prediction of aging-related genes from -omics data. Unlike gene expression methods for this task that capture aging-specific information but ignore interactions between genes (i.e., their protein products), or protein-protein interaction (PPI) network methods for this task that account for PPIs but the PPIs are context-unspecific, we recently integrated the two data types into an aging-specific PPI subnetwork, which yielded more accurate aging-related gene predictions. However, a dynamic aging-specific subnetwork did not improve prediction performance compared to a static aging-specific subnetwork, despite the aging process being dynamic. This could be because the dynamic subnetwork was inferred using a naive Induced subgraph

SUBMITTER: Li Q 

PROVIDER: S-EPMC8543111 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature