ArticleNucleic acids research2025
DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network.
Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Cell-type resolved transcriptional network analysis of in vivo cellular senescence following injury.PLoS computational biology · 2026Article
- Elucidating the Role of Oxidative Stress-Associated Genes FKBP Prolyl Isomerase 5 in Osteoarthritis Development and Immunological Milieu.Biological procedures online · 2026Article
- Multi-Omics Insights into Chronic Rhinosinusitis with Nasal Polyps: A Review of Lipidomics, Proteomics, and Transcriptomics.Journal of inflammation research · 2026Review
- In silico transcriptomic analysis nominates TSPAN32 as a central node of SCFA-driven immunometabolic reprogramming in intestinal epithelial cells.Frontiers in bioinformatics · 2026Article
- Proteomic Studies for the Identification and Characterization of Marine Bioactive Molecules.Marine drugs · 2025Article
- Interfered feature elimination coupled with feature group selection for wound infection detection by electronic nose.PloS one · 2025Article
- A multiple filter-wrapper feature selection algorithm based on process optimization mechanism for high-dimensional omics data analysis.PloS one · 2025Article
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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 network influence. Validated on prostate cancer datasets, DiCE identified genes overrepresented in key pathways and cancer fitness genes, significantly correlating with disease-free survival (DFS), despite DFS not being used in selection. DiCE offers a comprehensive, unbiased approach to identifying disease-associated genes, advancing biomarker discovery and therapeutic development.
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Registered trials
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