bioRxiv

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LazyNet: Interpretable ODE Modeling of Sparse CRISPR Single-Cell Screens Reveals New Biological Insights

bioRxiv Jan 1, 2025
We present LazyNet, a compact one-step neural-ODE model for single-cell CRISPR A/I that operates directly on two-snapshot (“pre → post”) measurements and yields parameters with clear mechanistic meaning. The core log–linear–exp residual block exactly...
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Dynamically Assembling Biological Intelligence to Predict Novel Cellular Phenotypes

bioRxiv Jan 1, 2025
In this work, we introduce Bio-AMLM (Biological Adaptive Modular Learning Model), a new framework designed to address out-of-distribution (OOD) generalization challenges in predicting cellular responses. Unlike monolithic deep learning models or simp...
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AI-guided competitive docking for virtual screening and compound efficacy prediction

bioRxiv Jan 1, 2025
Machine learning has transformed how we predict protein structures and interactions, but its full potential in drug discovery is only beginning to be realized. In this study, we demonstrate that advanced deep learning tools —such as AlphaFold3 and Bo...
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A simple circuit to sustain intact tumor microenvironments for complex drug interrogations

bioRxiv Jan 1, 2025
Deep learning and large language models can integrate complex datasets to uncover biological insights that are often undetectable through conventional analyses. With application to translational cancer research, these computational tools have positio...
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MAP-PRS: Multi-Ancestry Portfolio-Based Polygenic Risk Scores

bioRxiv Jan 1, 2025
Polygenic Risk Scores (PRS) are emerging tools for predicting an individual’s genetic risk for complex diseases. However, their usefulness in clinical practice remains limited because most existing models are based on data from people of European anc...
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DomDiff: protein family and domain annotation via diffusion model and ESM2 embedding

bioRxiv Jan 1, 2025
Accurate identification of conserved protein domain boundaries and their classification are fundamental to genome annotation, but are hindered by ambiguous boundaries, cross-domain interference, and limited samples for rare families. Here, we present...
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Improving the Accuracy of Distance-Based Protein–Ligand Binding Affinity Prediction Using Linear Regression and Artificial Neural Network1

bioRxiv Jan 1, 2025
In the traditional scoring functions for protein–ligand binding affinity prediction, the energies of the electrostatic and van der Waals interactions were evaluated (or restricted) by the mathematical expressions of and , respectively. In comparison...
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VirTrack: A Framework for Inferring Viral Influence on Disease-Associated Transcriptomes — Clinical Type-Specific Epstein–Barr Virus Pathogenesis in Multiple Sclerosis

bioRxiv Jan 1, 2025
Epstein–Barr virus (EBV) is strongly implicated in Multiple Sclerosis (MS), but how its influence varies across MS clinical types remains unclear. We developed VirTrack, a computational framework that integrates experimentally validated EBV–host prot...
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Application and Characterization of the Multiple Instance Learning Framework in Flow Cytometry

bioRxiv Jan 1, 2025
For decades, flow cytometry has allowed for single-cell profiling based on selected biomarkers and is widely used in both clinical and research settings. One major limitation of most conventional flow cytometry analyses is the dependency on a mostly ...
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Temporal stimulus segmentation by reinforcement learning in populations of spiking neurons

bioRxiv Jan 1, 2025
Learning to detect, identify or select stimuli is an essential requirement of many behavioral tasks. In real life situations, relevant and non-relevant stimuli are often embedded in a continuous sensory stream, presumably represented by different seg...
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