AIMC Journal:
bioRxiv

Showing 261 to 270 of 4935 articles

Neural dynamics of confidence formation under uncertainty

bioRxiv Sep 14, 2026
Our perceptual decision-making can vary depending not only on changes in the surrounding environment but also on metacognitive processes. In particular, the balance between confidence and uncertainty may play a critical role in shaping behavior and t...
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Harnessing the Intrinsic Dynamics of Biological Neural Networks for Reservoir Computing

bioRxiv Sep 14, 2026
Living neuronal networks exhibit nonlinear, recurrent, and evolving dynamics that make them promising substrates for reservoir computing, yet their computational use is complicated by spatial heterogeneity, spontaneous state transitions, and biologic...
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PHACTn enables training-free, context-independent inference of nucleotide variant tolerance across the genome

bioRxiv Sep 14, 2026
Accurate prediction of single-nucleotide variant (SNV) tolerability across the entire human genome remains a fundamental challenge in computational genomics, particularly for non-coding regions where the regulatory landscape is vast and poorly unders...
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miRAssist: a context-aware, evidence integration framework for interpretable miRNA-target prioritization

bioRxiv Sep 14, 2026
Motivation: MicroRNA-target interaction prediction remains challenging because many existing tools provide prediction scores or ranked candidate lists without making the supporting evidence easy to interpret or relate to a specific biological context...
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Reinforcement learning discovers new mechanisms of reentry in excitable media

bioRxiv Sep 14, 2026
The transition from transient excitation to sustained reentry is a fundamental problem in the physics of excitable media. In cardiac tissue, reentry underlies many life-threatening cardiac arrhythmias, yet the pathway to initiation of reentry remains...
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How effective are contrastive learning-based approaches for activity-cliff prediction?

bioRxiv Sep 14, 2026
Activity cliffs, defined as structurally similar molecules with vastly different properties represent a fundamental challenge in modern day drug discovery for property prediction models. While Graph Neural Networks (GNNs) have advanced molecular prop...
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Causal variant underestimation is a major overlooked driver of sequence-to-function model underperformance

bioRxiv Sep 11, 2026
Deep learning sequence-to-function (S2F) models represent tremendous promise for functionally fine-mapping causal variants associated with traits and disease. Yet it remains unclear precisely how effective they are at this task. Generally, S2F models...
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Reframing enzyme function prediction as conditional generation

bioRxiv Sep 11, 2026
Enzymes frequently exhibit promiscuous activity beyond their native roles, providing starting-points for new functions. Finding these promiscuous enzymes, especially for non-native chemical transformations, is challenging but highly valuable, as they...
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Machine-learning-guided enzyme discovery and redox-system engineering for efficient production of the nylon monomer methyl 12-aminododecanoate in Escherichia coli

bioRxiv Sep 11, 2026
Methyl 12-aminododecanoate (ADAME) is a key precursor for Nylon 12 synthesis and an attractive target for sustainable microbial production. However, efficient biosynthesis of ADAME requires the coordinated oxidation and transamination of methyl dodec...
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Classical baselines outperform released deep-learning ITS classifiers, which collapse on ITS2 where predictions follow the flanking regions

bioRxiv Sep 10, 2026
Background. Deep-learning classifiers for the fungal internal transcribed spacer (ITS) report accuracies above 90% and are increasingly proposed for environmental metabarcoding. That application differs from the benchmarks in two ways: surveys sequen...
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