Heterogeneous gene networks capture coordinated gene activities and systemic disruptions in complex biological processes and diseases, but extracting biologically meaningful insights from these large-scale networks remains challenging due to limited interpretability of existing methods. To address this gap, we have developed comprehensive information-based functional gene network analysis (CiFGNA), a novel computational methodology that systematically detects functional pathways enriched with phenotype-specific molecular interplays both in directed and undirected gene networks. CiFGNA characterizes the differential molecular interplay across phenotypes using probability density functions, quantifying network dissimilarities via Kullback–Leibler divergence. This approach incorporates both gene expression levels and network structures, enabling the accurate identification of phenotype-specific molecular interactions. We then ranked edges by their divergence scores and computed an enrichment score to evaluate whether pathway-associated molecular interactions were statistically overrepresented among highly divergent edges. By incorporating comprehensive gene network information and employing probability density functions with KL divergence as a dissimilarity measure, CiFGNA achieves accurate characterization of phenotype-specific molecular interactions, improving performance of gene network functional pathway analyses. Simulation and anticancer drug sensitivity analyses demonstrated that CiFGNA effectively identifies enriched cancer pathways and distinguishes molecular features associated with drug resistance and sensitivity. Key findings revealed gene networks centered on CD52, EPCAM, and TNFRSF12A as markers of drug-response phenotypes, suggesting that targeting resistance-related molecular interactions (e.g. CD52 and EPCAM) or enhancing sensitivity-associated markers such as TNFRSF12A may improve chemotherapy efficacy. Overall, CiFGNA offers a powerful, generalizable tool for interpreting complex gene networks and advancing systems-level understanding of disease mechanisms.
Park et al. (Fri,) studied this question.