A Computational Platform for Detecting Critical Instability Regimes in Biological Networks

A Computational Platform for Detecting Critical Instability Regimes in Biological Networks Author: Janos Gabor MeleghIndependent Researcher Platform proposalPrepared from existing study results on LUAD gene co-expression networks, TCGA-BRCA spectral cohesion, a unified cross-domain instability framework, and ABIDE functional brain connectivity. Abstract We propose that the published and preliminary findings obtained across cancer transcriptomes and functional brain connectivity justify the development of a general computational platform for detecting critical instability regimes in biological networks. Across these studies, disease-associated networks consistently show earlier fragmentation, reduced cohesion, or elevated instability relative to reference conditions. In LUAD gene co-expression networks, tumor networks fragment at lower correlation thresholds than normal lung under matched gene-set construction, with tumor r* = 0.420 ± 0.006 versus normal r* = 0.584 ± 0.006 for the first GCC