AIMC Journal:
bioRxiv

Showing 2621 to 2630 of 4979 articles

Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks

bioRxiv Jan 27, 2026
Protein-protein interactions (PPIs) are governed by two fundamental interfacial mechanisms: similarity-driven, often involving symmetric structural motifs, and complementarity-driven, arising from geometric and physicochemical matching between bindin...
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A stickiness scale for disordered proteins

bioRxiv Jan 27, 2026
Disordered proteins are a heterogeneous group of proteins that play a broad range of functions in biology, and display conformational properties that range from compact globules to expanded chains. We here describe the results of a data-driven approa...
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A brain dynamic model based on graph neural network reflect the inter-region interaction of cortical areas

bioRxiv Jan 27, 2026
A central objective in neuroscience is to elucidate how the brain generates complex dynamic activity through the interactions of brain areas. In this study, we utilized Interaction Network, a graph neural network model, to develop a computational fra...
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Deep Learning-Based Spatial Immunoprofiling of Multiplex Immunofluorescence Images Distinguishes Tuberculosis Disease States in Diversity Outbred Mice

bioRxiv Jan 27, 2026
Tuberculosis (TB), caused by Mycobacterium tuberculosis (M.tb), remains a major global health challenge, with approximately 10.8 million new cases and 1.25 million deaths reported in 2023. Human responses to M.tb are heterogeneous with clinical outco...
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UdonPred: Untangling Protein Intrinsic Disorder Prediction

bioRxiv Jan 27, 2026
Motivation: Regions in intrinsic disordered proteins (IDPs) constitute important continuous aspects of protein function. While their existence on a structural continuum is widely accepted, most computational predictions have, nevertheless, focused on...
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End-to-end deep learning versus machine learning for biomarker discovery in cancer genomes

bioRxiv Jan 26, 2026
Background: Accurate determination of genomic biomarkers from tumor sequencing is fundamental to precision oncology, informing disease classification and treatment decisions. In practice, biomarker inference relies on computational pipelines that oft...
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HYALINE: Geometric Deep Learning for Accurate Prediction of G Protein-Coupled Receptor Activation States from Structure

bioRxiv Jan 26, 2026
Characterizing the conformational landscapes of G protein-coupled receptors (GPCRs) is fundamental to understanding signal transduction and accelerating rational drug design. However, current computational approaches often rely on static sequence ana...
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Predicting Gene Disease Associations in Type 2 Diabetes Using Machine Learning on Single-Cell RNA-Seq Data

bioRxiv Jan 26, 2026
Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels due to impaired insulin production or function. Two main forms are recognized: type 1 diabetes (T1D), which involves autoimmune destruction of insulin-producing {...
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ProChoreo: De novo Binder Design from Conformational Ensembles with Generative Deep Learning

bioRxiv Jan 26, 2026
Deep learning has transformed protein structure prediction and de novo protein design; however, most existing frameworks operate on a single static conformation and underutilize the conformational heterogeneity that governs protein binding and functi...
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Temporal Dynamics of EEG Decoding for Continuously Changing Visual Stimuli

bioRxiv Jan 26, 2026
Multivariate analyses of M/EEG data are typically performed on neural responses time-locked to discrete stimulus onsets. Such designs usually reveal high decoding performance during the initial transient response (0-500 ms), which subsequently drops ...
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