AIMC Journal:
bioRxiv

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Comparative Evaluation of Deep Generative Models for Capturing Topological Features in Brain Structural Connectivity

bioRxiv Jun 8, 2026
Structural connectivity (SC) data are crucial for brain network analysis, but SC-based machine learning often suffers from limited data availability, hindering model generalization and robustness. Although data augmentation using deep generative mode...
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ANNet: A first-principles neural network for forward and inverse dynamics

bioRxiv Jun 8, 2026
Biological and robotic systems must solve two related computations to move: inverse dynamics, which determines the forces or torques needed to produce a desired movement, and forward dynamics, which maps applied forces to motion. Although these compu...
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Comprehensive evaluation of LLM capabilities for interpretation and analysis of genome-scale metabolic models in metabolic engineering

bioRxiv Jun 8, 2026
Genome-scale metabolic models (GSMs) underpin pathway and strain engineering by linking genes to metabolic reactions and enabling system-level simulation of cellular fluxes and intervention effects, yet end-to-end analysis workflows remain fragmented...
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Motor Resonance of Musical Emotion: A Machine Learning Approach to EEG Decoding During Expressive Music Performance

bioRxiv Jun 8, 2026
Understanding the neural dynamics underlying expressive musical performance remains a major challenge at the intersection of neuroscience, music cognition, and computational modeling. While EEG studies of emotion have largely focused on passive expos...
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Comparing Machine Learning Approaches for Predicting CFD-Derived Stroke Risk Indicators in Atrial Fibrillation Patients

bioRxiv Jun 8, 2026
Non-valvular atrial fibrillation (AF) is associated with a five-fold increased risk of stroke, mainly due to impaired contractility of the left atrium (LA) leading to blood stasis and subsequent thrombus formation within the left atrial appendage (LA...
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TomoScore: A Neural Network Approach for Quality Assessment of Cellular cryo-ET

bioRxiv Jun 8, 2026
Electron cryo-tomography (cryoET) is a powerful imaging tool that allows three-dimensional visualization of subcellular and molecular architecture without chemical fixation. Tomogram quality varies widely, particularly during large high-throughput da...
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A Drug-Target Specificity Foundation Model for Off-target Prediction, Repurposing, and Generative Design

bioRxiv Jun 8, 2026
Molecular recognition - which small molecule binds which protein, and with what selectivity - governs the efficacy, safety, and discovery of every therapeutic, yet binding specificity is still determined by experimental screening or by computational ...
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Stack: In-Context Learning of Single-Cell Biology

bioRxiv Jun 8, 2026
Foundation models trained on single-cell transcriptomic data offer the promise of identifying and predicting the diversity of cellular phenotypes across species, diseases, and other biological conditions. However, the current models are limited to th...
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CascadeMAP: Autonomous Closed-loop Optimization of Enzyme Cascades via Microfluidics, Machine Learning and Agentic AI

bioRxiv Jun 7, 2026
Enzyme cascades enable complex biochemical transformations, but their optimization is resource-intensive, requiring navigation through high-dimensional parameter spaces encompassing reaction conditions, enzyme ratios, and buffer composition. Here we ...
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Learning quality scores for chromatin accessibility bigWig tracks using Machine Learning

bioRxiv Jun 7, 2026
High-throughput chromatin accessibility assays such as bulk and single-cell ATAC-seq have generated large collections of processed signal tracks in bigWig format, which are widely used for visualisation, data integration, and Machine Learning (ML)-ba...
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