AIMC Journal:
bioRxiv

Showing 201 to 210 of 4935 articles

Predictive coding networks capture human neural representations missing in supervised DNNs

bioRxiv Sep 24, 2026
Neuroscientific learning theories propose that the brain acquires knowledge by constructing internal world models. Supervised learning, the dominant approach in deep neural networks (DNN), relies on external category labels, making it difficult to re...
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A Taxonomy-Informed Sparse DNA Foundation Model for Microbial Genomics

bioRxiv Sep 24, 2026
Microorganisms are crucial to Earth's ecosystems, with genomes encoding functions important to agriculture, biotechnology and human health. Although genomic language models have advanced DNA representation learning, the extensive diversity of microor...
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CardioChrom Reconstructs the Human Cardiac Virtual Epigenome from Single-Nucleus Transcriptomes

bioRxiv Sep 24, 2026
Background: Matched epigenomic profiling remains scarce in human heart failure, limiting regulatory interpretation of single-nucleus ribonucleic acid (RNA) sequencing data. We developed CardioChrom, a cardiac-specialized framework that reconstructs c...
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Ultrastructural comparison of fixation and cryopreservation methods for brain preservation

bioRxiv Sep 24, 2026
Maximizing the morphological and molecular fidelity of preserved mammalian brain tissue is essential for basic neuroscience and brain banking, where tissue quality determines the reliability of downstream analyses. Aldehyde fixation and cryogenic sto...
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CMAC: A deep learning framework for absolute cardiomyocyte transcriptional age estimation

bioRxiv Sep 24, 2026
Cardiomyocyte maturation spans embryogenesis through postnatal life and involves coordinated transcriptional transitions that are disrupted in disease and incompletely recapitulated by stem cell-derived cardiomyocytes. However, no unified framework e...
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Deciphering Mechanistic Signatures in Drug-Drug Interactions with Dual Topology Graphs

bioRxiv Sep 24, 2026
Drug-drug interactions (DDIs) represent a critical challenge in drug development and clinical practice, as they can lead to severe adverse effects, including toxicity and reduced therapeutic efficacy. Deep learning methods have shown promise in large...
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Predicting single mutation effects on binding affinity at protein-protein interfaces based on MMPBSA calculations

bioRxiv Sep 24, 2026
Accurate prediction of mutation-induced changes in protein-protein affinity remains a central challenge in computational biophysics and protein engineering. Here we present a physics-based scoring method for predicting the effects of single amino aci...
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NeuralRNN: a unified framework for recurrent neural network methods in cognitive neuroscience

bioRxiv Sep 21, 2026
Recurrent neural networks (RNNs) provide a central tool in neuroscience to optimize cognitive tasks and reconstruct dynamical systems. Around these two paradigms, many RNN model variants have been tailored to capture specific computations of the brai...
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Exploring Healthy Neurocognitive Ageing with Deep Learning Interpretability Methods

bioRxiv Sep 21, 2026
Healthy ageing reorganises large-scale functional brain networks. Using resting-state fMRI from 615 adults aged 18 to 88 years in Cam-CAN. We combined raw functional connectivity analyses, graph-theoretical topology, Ridge and MLP age prediction and ...
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Integrating structural homology with deep learning to achieve highly accurate protein-protein interface prediction for the human interactome

bioRxiv Sep 21, 2026
A significant portion of disease-causing mutations occur at protein-protein interfaces however, the number of structurally resolved multi-protein complexes is extremely small. Here we present a computational pipeline, PIONEER2, that integrates 3D str...
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