bioRxiv

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BLMPred: predicting linear B-cell epitopes using pre-trained protein language models and machine learning

bioRxiv Jan 1, 2025
B-cells get activated through interaction with B-cell epitopes, a specific portion of the antigen. Identification of B-cell epitopes is crucial for a wide range of clinical applications, including disease diagnostics, vaccine and antibody development...
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SAM-DTI: A Spatial Attention Model for Drug-Target Interaction Prediction

bioRxiv Jan 1, 2025
Drug-target protein interaction (DTI) prediction is an important area of research in drug repurposing and discovery. Laboratory experiments for exploring DTI space need a lot of time and money due to the high volume of potential compounds. The past d...
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Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

bioRxiv Jan 1, 2025
Spatial transcriptomics technologies enable high-throughput quantification of gene expression at specific locations across tissue sections, facilitating insights into the spatial organization of biological processes. However, high costs associated wi...
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Modanovo: A Unified Model for Post-Translational Modification-Aware de Novo Sequencing Using Experimental Spectra from In Vivo and Synthetic Peptides

bioRxiv Jan 1, 2025
Post-translational modifications (PTMs) play a central role in cellular regulation and are implicated in numerous diseases. Database searching remains the standard for identifying modified peptides from tandem mass spectra, but is hindered by the com...
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Personalized real-time inference of momentary excitability from human EEG

bioRxiv Jan 1, 2025
The efficacy of transcranial magnetic stimulation (TMS) is often limited by non-adaptive protocols that disregard instantaneous brain states, potentially constraining therapeutic outcomes. Current EEG-guided approaches are hindered by their reliance ...
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ScAPE: A lightweight multitask learning baseline method to predict transcriptomic responses to perturbations

bioRxiv Jan 1, 2025
Predicting biological responses to perturbations is essential for advancing drug development efforts. While high-throughput technologies have greatly enhanced our ability to study perturbation effects at the molecular level, accurately modeling these...
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Interpretable Machine Learning and Comparative Genomics Reveal Microbial Plastic-Degrading (Microbeyt) Potential

bioRxiv Jan 1, 2025
Plastic pollution poses a critical environmental threat, and microbial enzymes represent a sustainable strategy for polymer degradation. We present a computational pipeline that integrates orthogroup-based genomic analysis with machine learning and i...
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Memory-specific E-I balance supports diverse replay and mitigates catastrophic forgetting

bioRxiv Jan 1, 2025
The hippocampus is the brain’s central locus for memory processing. In a widely accepted hypothesis, the hippocampus stores short-term memories in attractor states, which are then consolidated in the neocortex as long-term memories. Hippocampal repla...
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SpaPheno: Linking Spatial Transcriptomics to Clinical Phenotypes with Interpretable Machine Learning

bioRxiv Jan 1, 2025
Linking spatial transcriptomic data to clinically relevant phenotypes is essential for advancing spatially informed precision oncology. Here, we present SpaPheno, an interpretable machine learning framework that integrates spatial transcriptomics wit...
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Dietary Protein Source Shapes Gut Microbial Structure and Predicted Function: A Meta-Analysis with Machine Learning

bioRxiv Jan 1, 2025
Dietary proteins are major modulators of gut microbial ecology, yet the microbial signatures and functional consequences of plant-versus animal-based proteins remain poorly defined. Although digestibility and fermentation profiles differ by protein t...
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