DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network.

ABSTRACT: Uncovering key genes that drive diseases and cancers is crucial for advancing understanding and developing targeted therapies. Traditional differential expression analysis often relies on arbitrary cutoffs, missing critical genes with subtle expression changes. Some methods incorporate protein-protein interactions (PPIs) but depend on prior disease knowledge. To address these challenges, we developed DiCE (Differential Centrality-Ensemble analysis), a novel approach that combines differential expression with network centrality analysis, independent of prior disease annotations. DiCE identifies candidate genes, refines them with an information gain filter, and reconstructs a condition-specific weighted PPI network. Using centrality measures, DiCE ranks genes based on expression shifts and n

SUBMITTER: Pashaei E 

PROVIDER: S-EPMC12235518 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature