AIMC Journal:
bioRxiv

Showing 2861 to 2870 of 4993 articles

Cell type–specific functions of nucleic acid-binding proteins revealed by deep learning on co-expression networks

bioRxiv Jan 1, 2025
Nucleic acid-binding proteins (NABPs) exhibit cell type–specific regulatory functions, but their target genes and biological roles remain incompletely characterized due to the limitations of current experimental approaches. Here, we present a deep le...
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Evaluating large language models in biomedical data science challenges through a classroom experiment

bioRxiv Jan 1, 2025
Large language models have shown remarkable capabilities in algorithm design, but their effectiveness in solving data science challenges remains poorly understood. We conducted a classroom experiment in which graduate students used large language mod...
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Quantum analysis of protein–ligand binding by integrating structural resolution, sequence homology, and ligand properties

bioRxiv Jan 1, 2025
Predicting protein–ligand binding affinity is a fundamental challenge in computational biology and drug discovery, complicated by diverse factors including protein sequence variability, ligand chemical diversity, and structural resolution. Here, we p...
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RNALens: Study on 5’ UTR Modeling and Cell-Specificity

bioRxiv Jan 1, 2025
Recently, the Transformer architecture has been applied to predict the structure, function, and regulatory activity of biological sequences. Predicting the cell-specific regulatory impact of 5’ untranslated regions (5’ UTRs) on mRNA expression and tr...
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Matrix effects influence biochemical signatures and metabolite quantification in dried blood spots

bioRxiv Jan 1, 2025
Dried blood spots (DBS) represent a convenient clinical sample material, offering low infection risk, easy transport, and long-term metabolite stability. However, applying samples such as whole blood, serum or plasma onto filter paper introduces an a...
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Delta Marches to autonomously learn histopathology rules by generative latent space traversals

bioRxiv Jan 1, 2025
Deep learning (DL) has excelled in tissue image classification, presenting opportunities to discover biological behaviors escaping visual inspection. However, the methods to produce fine-grained insights from spatially scattered information do not ex...
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Applying gradient tree boosting to QTL mapping with Shapley additive explanations

bioRxiv Jan 1, 2025
Mapping quantitative trait loci (QTLs) is one of the major goals of quantitative genetics; however, identifying the interactions between QTLs (i.e., epistasis) remains challenging. Recently developed machine learning methods, such as deep learning an...
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PromoterAtlas: decoding regulatory sequences across Gammaproteobacteria using a transformer model

bioRxiv Jan 1, 2025
Recent advances in deep learning, particularly transformer architectures, have improved computational approaches for biological sequence analysis. Despite these advances, computational models for bacterial promoter prediction have remained limited by...
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Inception: Simulating Personalized Long-Term Recovery in Disorders of Consciousness using Whole-Brain Computational Perturbations

bioRxiv Jan 1, 2025
Advancements in the treatment of Disorders of Consciousness have seen significant progress with per-turbative techniques and pharmacological therapies. Despite their potential, the underlying mechanisms of their variable efficacy remain poorly unders...
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Deep Learning for genomic prediction accounting for heterosis in crossbreeding systems

bioRxiv Jan 1, 2025
Crossbreeding is used in animal breeding to combine desirable traits from different breeds and to exploit hybrid vigor, and many approaches have been proposed to improve the prediction of genetic values of crossbred animals. This study aimed to asses...
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