Network-based gene prioritization using hybrid scoring for complex disease module discovery

Abstract Complex diseases arise from perturbations in interconnected biological networks rather than isolated genetic defects. Network-based approaches provide a systematic framework for understanding disease mechanisms through protein-protein interaction data, yet most existing methods are developed and validated on a single disease or pathogenic mechanism, leaving their generalizability largely untested. We present a disease-agnostic computational framework that integrates five biological databases to construct robust ground truth gene sets, applies sensitivity analysis to identify high-confidence disease genes, and combines network topology with diffusion algorithms for systematic gene prioritization, requiring only a disease name as input. Our approach employs a noisy-OR fusion strategy to integrate evidence from DISEASES, GeneCards, OpenTargets, Gene2Phenotype, and NCBI Gene, followed by genetic algorithm optimization and random walk with restart for gene prioritization. Disease-relevant subnetworks were extracted and analyzed using complementary clustering algorithms (Leiden and MCL) to identify modules validated through pathway enrichment analysis. Without disease-specific tuning, the same workflow was applied unchanged to two neurodegenerative disorders and one autoimmune disease, chosen to span markedly different pathogenic mechanisms: Alzheimer disease (AD), Parkinson disease (PD), and rheumatoid arthritis (RA). In each case the framework recovered known disease genes and identified biologically coherent, disease-specific modules: in AD, 17 stable high-confidence genes and modules enriched in lipid and cholesterol metabolism and amyloid precursor protein processing; in PD, 17 stable genes and modules associated with mitophagy, ubiquitin-proteasome signaling, and mitochondrial dysfunction; in RA, 20 stable genes and modules enriched in antigen presentation and JAK-STAT cytokine signaling. This consistent recovery of mechanistically distinct, biologically appropriate signatures from a single unmodified pipeline demonstrates that the framework generalizes across disease categories rather than being tuned to any one of them. By prioritizing biological validation through pathway enrichment over predictive accuracy, and by demonstrating consistent performance across neurodegenerative and autoimmune contexts without disease-specific adaptation, the framework offers a versatile, readily extensible tool for exploratory analyses of disease mechanisms, including diseases for which prior mechanistic knowledge is limited. All code, data, and results are publicly available to ensure reproducibility.