bioRxiv

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Overcoming Extrapolation Challenges of Deep Learning by Incorporating Physics in Protein Sequence-Function Modeling

bioRxiv Jan 1, 2025
Understanding protein sequence-to-function relationship is crucial to assist studies of genetic diseases, protein evolution, and protein engineering. The sequence-to-function relationship of proteins is inherently complex due to multi-site high-dimen...
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CochleaNet: deep learning-based image analysis for cochlear connectomics and gene therapy

bioRxiv Jan 1, 2025
With the emergence of gene and optogenetic therapies targeting deafness, the comprehensive analysis of the molecular anatomy and physiology of the cochlea has become ever more important. Here, we introduce CochleaNet, a deep learning-based framework ...
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Prebiotically Plausible Peptides can Self-assemble into β-rich Nanostructures

bioRxiv Jan 1, 2025
Modern proteins are remarkable polymers built from a 20-amino-acid alphabet, shaped by billions of years of evolution. Yet in Earth’s prebiotic era, several amino acids – particularly the canonical basic residues lysine, arginine, and histidine – wer...
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Atacformer: A transformer-based foundation model for analysis and interpretation of ATAC-seq data

bioRxiv Jan 1, 2025
Chromatin accessibility profiling is an important tool for understanding gene regulation and cellular function. While public repositories house nearly 10,000 scATAC-seq experiments, unifying this data for meaningful analysis remains challenging. Exis...
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Systematic evaluation of peptide property predictors with explainable AI technique SHAP

bioRxiv Jan 1, 2025
Deep learning models are often characterized as black boxes because their layers of various mathematical transformations and activation functions are practically uninterpretable and have little meaning to the user. However, explainable AI methods exi...
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GeneCAD: Plant Genome Annotation with a DNA Foundation Model

bioRxiv Jan 1, 2025
Accurate genome annotation remains a bottleneck in plants, where polyploidy and repeat-rich sequence confound homology- and RNA-based pipelines. We introduce GeneCAD, a sequence-only method that predicts complete plant gene models directly from DNA. ...
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Inhibitory cell type heterogeneity in a spatially structured mean-field model of V1

bioRxiv Jan 1, 2025
Inhibitory interneurons in the cortex are classified into cell types differing in their morphology, electrophysiology, and connectivity. Although it is known that parvalbumin (PV), somatostatin (SST), and vasoactive intestinal polypeptide-expressing ...
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Non-decision time-informed collapsing threshold diffusion model: A joint modeling framework with identifiable time-dependent parameters

bioRxiv Jan 1, 2025
Over the past sixty years, evidence accumulation models have emerged as a dominant framework for explaining the neural and behavioral aspects of the process underlying decision making. These models have also been widely used as a measurement instrume...
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Decoding the neural stages from action and object recognition to mentalizing

bioRxiv Jan 1, 2025
Higher-level action interpretation, such as inferring underlying intentions and predicting future actions, requires the integration of conceptual action information (e.g. "opening") with semantic knowledge about persons and objects (e.g. "my friend A...
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SpecLig: Energy-Guided Hierarchical Model for Target-Specific 3D Ligand Design

bioRxiv Jan 1, 2025
Structure-based generative models often optimize single-target affinity with ignorance of specificity, resulting in the generation of high-affinity candidates that exhibit promiscuous binding across unrelated targets. This decoupling of affinity and ...
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