Akademik Veri Yönetim Sistemi
Biomedicines, cilt.14, sa.1, 2026 (SCI-Expanded, Scopus)
Background/Objectives: Autism spectrum disorder (ASD) exhibits remarkable genetic heterogeneity involving hundreds of risk genes; however, the mechanism by which these genes organize within biological networks to contribute to disease pathogenesis remains incompletely understood. This study aims to elucidate these organizational principles and identify critical network bottlenecks using a novel integrative computational framework. Methods: We analyzed 893 SFARI genes using a three-pronged computational approach: (1) a Machine Learning Dynamic Perturbation Propagation algorithm; (2) a hypergraph construction method explicitly modeling multi-gene complexes by integrating protein–protein interactions, co-expression modules, and curated pathways; and (3) Hypergraph Neural Network embeddings for gene clustering. Validation was performed using hub-independent features to address potential circularity, followed by a druggability assessment to prioritize therapeutic targets. Results: The hypergraph construction captured 3847 multi-way relationships, representing a 45% increase in biological relationships compared to pairwise networks. The perturbation algorithm achieved a 51% higher correlation with TADA genetic evidence than random walk methods. Analysis revealed a hierarchical organization where 179 hub genes exhibited a 3.22-fold increase in degree centrality and a 4.71-fold increase in perturbation scores relative to non-hub genes. Hypergraph Neural Network clustering identified five distinct gene clusters, including a “super-hub” cluster of 10 genes enriched in synaptic signaling (4.2-fold) and chromatin remodeling (3.9-fold). Validation confirmed that 8 of these 10 genes co-cluster even without topological information. Finally, we identified high-priority therapeutic targets, including ARID1A, POLR2A, and CACNB1. Conclusions: These findings establish hierarchical network organization principles in ASD, demonstrating that hub genes maintain substantially elevated perturbation states. The identification of critical network bottlenecks and pharmacologically tractable targets provides a foundation for understanding autism pathogenesis and developing precision medicine approaches.