2026· International Conference on Conceptual Structures· pp. 159-171· 0 citations· 26 references
Computer Science
TL;DR
These findings support network curvature as a general, domain-independent indicator of biological network dysfunction, highlighting its potential to reveal fundamental geometric signatures of disease that are not captured by conventional connectivity measures.
BACKGROUND
Excessive adiposity drives cardiometabolic morbidity through macroscopic systemic mechanisms rather than isolated metabolic defects. While conventional statistical models effectively estimate marginal biomarker associations, they cannot resolve the directed and cyclic regulatory dependencies underlying this pathogenesis. Network physiology offers a robust mathematical framework to delineate these complex, feedback-driven interactions.
METHODS
Applying the idopNetwork framework to cross-sectional data from a primary clinical cohort (N=7,343) and an external validation cohort (N=6,881), we reconstructed directed, weighted hematometabolic networks across four body mass index (BMI) strata. This analytical pipeline mapped static clinical snapshots onto a continuous coordinate space to extract quasi-dynamic autoregressive trajectories. GLMY path homology was subsequently integrated to quantify macroscopic network rigidity via higher-order topological features.
RESULTS
Progressive adiposity manifested a graded topological reorganization along the allostatic load gradient, characterized by the functional polarity inversion of core physiological hubs. Uric acid initially centralized as a primary conduit of metabolic load, preceding the transition of hemoglobin from a homeostatic anchor into a predictive promoter of systemic inflammation. GLMY path homology defined advanced obesity as a rigid systemic gridlock, quantified by the abnormal accumulation of persistent one-dimensional cyclic loops (β1) and two-dimensional voids (β2). This terminal architecture exhibited profound sexual dimorphism. Male adiposity degraded into hyper-reactive inflammatory β1 cyclic tangling, whereas the female network experienced a near-complete depletion of preexisting β2 voids upon entering advanced obesity, culminating in extensive topological barrenness and the attenuation of compensatory feedback.
CONCLUSIONS
The internal physiological ecosystem structurally deteriorates from robust homeostatic buffering into an advanced, sexually dimorphic allostatic deadlock. By decoupling directed regulatory flows from systemic confounding, this macroscopic algebraic approach challenges universal treatment protocols, highlighting the necessity for sex-specific precision interventions in managing obesity-driven cardiometabolic risk.
Unknown authors· Computers in Biology and Med...· 0 citations
Gene network analysis is critically implicated in disease research for uncovering functional modules and interaction-driven pathways underlying biological and disease processes. However, the interpretation of large inferred networks remains challenging. Although functional gene network analysis allows the interpretation of large inferred networks, challenges such as reduction of multiple network-level features to a single composite score often limit their application. This data reduction can mask the important multivariate characteristics of gene networks, hindering efficient differentiation of individual contributions of distinct network components. Hence, this study aimed to investigate a novel computational strategy called Multivariate Framework for Functional Gene Network Enrichment Analysis (mFGNA). This framework incorporated diverse network-level features from a graph-theoretical perspective, including node properties (centrality), edge connectivity patterns (Jaccard distance), interaction strengths (edge weights), alongside traditional expression levels. Notably, mFGNA preserved these multidimensional characteristics, capturing complex rewiring of gene networks across different phenotypic states. Furthermore, mFGNA adopted a gene-level permutation strategy to evaluate the enrichment hypothesis, ensuring effective statistical inference and reduced computational complexity compared with phenotype-based permutations. Extensive Monte Carlo simulations validated mFGNA through both undirected and directed gene networks, showing consistently improved performance over existing approaches across diverse pathway settings. We also applied mFGNA to investigate immune pathway perturbations in cancer cell lines and identified significant network-level dysregulation in pancreatic and non-small cell lung cancers. Cancer-specific interaction modules were dominated by human leukocyte antigen class II genes. Meanwhile, normal cell networks were characterized by hub genes such as MMP1 and MMP3 that were implicated in tissue maintenance, highlighting immune remodeling in tumors and the potential molecular targets for developing diagnostic and therapeutic interventions. Overall, the study shows that mFGNA enables effective functional pathway discovery in complex gene networks, providing mechanistic insights and potential translational targets in disease contexts.
Heewon Park, S. Imoto· Frontiers in Genetics· 0 citations
This work introduces an alternative criterion based on the persistent topological cycles in which each node participates---a measure of mesoscale integration that captures features beyond local connectivity---and demonstrates that persistent topology captures information about brain network control that scalar energy summaries miss.
Carter Sale, Marco Coraggio, Mengsen Zhang et al.· 0 citations
Omics data, such as transcriptomics, proteomics, and metabolomics, provide critical insights into disease mechanisms and clinical outcomes. However, their high dimensionality, small sample sizes, and intricate biological networks pose major challenges for reliable prediction and meaningful interpretation. Graph Neural Networks (GNNs) offer a promising way to integrate prior knowledge by encoding feature relationships as graphs. Yet, existing methods typically rely solely on either an externally curated feature graph or a data-driven generated one, which limits their ability to capture complementary information. To address this, we propose the external and generated Graph Neural Network (engGNN), a dual-graph framework that jointly leverages both external known biological networks and data-driven generated graphs. Specifically, engGNN constructs a biologically informed undirected feature graph from established network databases and complements it with a directed feature graph derived from tree-ensemble models. This dual-graph design produces more comprehensive embeddings, thereby improving predictive performance and interpretability. Through extensive simulations and real-world applications to gene expression data, engGNN consistently outperforms state-of-the-art baselines. Beyond classification, engGNN provides interpretable feature importance scores that facilitate biologically meaningful discoveries, such as pathway enrichment analysis. Taken together, these results highlight engGNN as a robust, flexible, and interpretable framework for disease classification and biomarker discovery in high-dimensional omics contexts.
Unknown authors· Briefings in Bioinformatics· 0 citations
Motivation Kinases regulate a multitude of protein functions, and their dysregulation is pivotal for many human diseases. Direct measurement of kinase activity, however, is often challenging; therefore, inferring activity from the behavior of their substrates is a widely adopted strategy. Nonetheless, traditional methods typically oversimplify the underlying network, ignoring that any particular substrate can be phosphorylated by multiple kinases. Results We present LIKA, a likelihood-based framework for inferring kinase activity from phosphoproteomic data. By modeling the many-to-many structure of kinase-substrate interactions, LIKA achieves high efficiency, even with limited data, while capturing network complexity. Simulation and cell line analyses confirm the robustness and accuracy of LIKA. Importantly, analysis of a phosphoproteomic dataset from schizophrenia and control subjects reveals novel dysregulated kinases. Availability and Implementation The implementation code and publicly available data are provided at: https://github.com/lujingz/LIKA.
Lujing Zhang, A. DeMarco, Kimia Ghafari et al.· bioRxiv· 0 citations
ABSTRACT Background and Aims How Crohn's disease alters large‐scale brain network organization and interacts with affective symptoms remains poorly understood. We aimed to characterize disease‐related disruptions in functional topology, rich‐club organization, and hierarchical gradients, and to determine their associations with disease activity and anxiety. Methods Ninety‐seven Crohn's disease patients and 64 healthy controls underwent resting‐state fMRI. Graph‐theoretical metrics, rich‐club analysis, functional gradients, and spectral dynamic causal modeling were applied to assess network properties, hierarchical architecture, and effective connectivity. Associations with the Crohn's Disease Activity Index and anxiety were examined via linear regression. Results Crohn's disease patients showed widespread network disruptions modestly linked to disease activity, including reduced global and local efficiency, impaired rich‐club connectivity, and compressed sensorimotor gradients. Decreased degree centrality in the insula, thalamus, and postcentral gyrus was accompanied by altered hierarchical organization and effective connectivity. Spectral dynamic causal modeling revealed anxiety‐related modulation of insular excitation and thalamo‐putaminal pathways. Conclusions Crohn's disease involves widespread functional network disruption and reorganization, particularly in interoceptive regions supporting sensory and emotional processing. Anxiety modulates core limbic circuitry, highlighting the interplay between disease activity and affective symptoms in shaping brain network.
