bioRxiv

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DeepSpot2Cell: Predicting Virtual Single-Cell Spatial Transcriptomics from H&E images using Spot-Level Supervision

bioRxiv Jan 1, 2025
Spot-based spatial transcriptomics (ST) technologies like 10x Visium quantify genome-wide gene expression and preserve spatial tissue organization. However, their coarse spot-level resolution aggregates signals from multiple cells, preventing accurat...
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Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network

bioRxiv Jan 1, 2025
Pyrazinamide is an important first-line antibiotic for treating tuberculosis and resistance is primarily caused by mutations in the pncA gene. Traditional machine learning models have been shown to be able to predict pyrazinamide resistance with some...
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Evolutionary Reasoning Does Not Arise in Standard Usage of Protein Language Models

bioRxiv Jan 1, 2025
Protein language models (PLMs) are often assumed to capture evolutionary information by training on large protein sequence datasets. Yet it remains unclear whether PLMs can reason about evolution—that is, infer evolutionary relationships between sequ...
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ConforFold Recovers Alternative Protein Conformations Beyond MSA Subsampling

bioRxiv Jan 1, 2025
Conformational changes underlie many aspects of protein function, yet current structure prediction tools remain limited in their ability to systematically sample structural ensembles. Here, we present ConforPSSP and ConforFold, a combined framework t...
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Reinforcement Failing guides the discovery of emergent physical dynamics in adaptive tumor therapy

bioRxiv Jan 1, 2025
Artificial intelligence is revolutionizing scientific discovery in medicine, with reinforcement learning (RL) emerging as a promising tool for optimizing therapeutic strategies. Yet applying RL to complex scenarios such as therapy dynamics in solid t...
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Multi-modal data integration for machine learning applications

bioRxiv Jan 1, 2025
The integration of multi-modal genomic data, encompassing sequences, annotations, and coverage tracks, remains a major bottleneck in bioinformatics, both for exploratory data analysis and machine learning applications. Current approaches rely on seve...
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Deep Learning-Based Classification of Colorectal Cancer in Histopathology Images for Category Detection

bioRxiv Jan 1, 2025
Accurate and timely diagnosis of colorectal cancer (CRC) is essential for effective treatment and better patient outcomes. This study explores the application of deep learning (DL) for automated CRC categories classification using hematoxylin and eos...
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Decoding the Energetic Logic of Genetic Systems: A Hybrid Neural-Symbolic Framework for Quantum-Informed Bioenergetics

bioRxiv Jan 1, 2025
Life is sustained by the dynamic flow of energy through adenosine triphosphate (ATP), redox carriers such as NADH, and reactive oxygen species (ROS). These molecules not only fuel biochemical reactions but also encode information that regulates gene ...
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Controlling Payload Heterogeneity in Lipid Nanoparticles for RNA-Based Therapeutics

bioRxiv Jan 1, 2025
Lipid nanoparticles (LNPs) are a leading platform for nucleic acid delivery, yet conventional assembly by mixing lipids with RNA yields particles with heterogeneous, bimodal payload distribution. Our transfection experiments demonstrate that heteroge...
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A corticostriatal learning mechanism linking excess striatal dopamine and auditory hallucinations

bioRxiv Jan 1, 2025
Auditory hallucinations are linked to elevated striatal dopamine, but their underlying computational mechanisms have been obscured by regional heterogeneity in striatal dopamine signaling. To address this, we developed a normative circuit model in wh...
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