bioRxiv

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CATERPillar: A Flexible Framework for Generating White Matter Numerical Substrates with incorporated Glial Cells

bioRxiv Jan 1, 2025
Monte Carlo diffusion simulations in numerical substrates are valuable for exploring the sensitivity and specificity of the diffusion MRI (dMRI) signal to realistic cell microstructure features. A crucial component of such simulations is the use of n...
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RPEGENE-Net: A Multi-Resolution Deep Learning Framework for Predicting Gene Expression from Microscopy Images of Retinal Pigment Epithelium (RPE) Cells

bioRxiv Jan 1, 2025
To develop a deep learning framework, RPEGENE-Net, capable of predicting gene expression profiles of retinal pigment epithelium (RPE) cells using live-cell microscopy images. A dataset of live-cell images of RPE cells, treated with various drug regim...
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Accelerating Drug Discovery with HyperLab: An Easy-to-Use AI-Driven Platform

bioRxiv Jan 1, 2025
HyperLab, developed by HITS, is a web-based, AI-driven drug discovery platform designed to increase research efficiency for experimental drug discovery researchers. The platform features an intuitive user interface and experience (UI/UX), enabling re...
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A Systematic Comparison of Single-Cell Perturbation Response Prediction Models

bioRxiv Jan 1, 2025
Predicting single-cell transcriptional responses to perturbations is central to dissecting gene regulation and accelerating therapeutic design, yet the field lacks a rigorous, task-spanning assessment of model behavior. We present a large-scale bench...
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Pathologist-interpretable breast cancer subtyping and stratification from AI-inferred nuclear features

bioRxiv Jan 1, 2025
Artificial intelligence (AI) is making notable advances in digital pathology but faces challenges in human interpretability. Here we introduce EXPAND (EXplainable Pathologist Aligned Nuclear Discriminator), the first pathologist-interpretable AI mode...
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A framework to detect positive selection using variant effect predictions reveals widespread adaptive evolution of human neurons

bioRxiv Jan 1, 2025
Detecting positive selection is essential to understanding evolution. Many methods to detect positive selection use simple classifications of genetic variants (e.g. synonymous/nonsynonymous). Here, we propose that these methods can be considered spec...
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The Darwin–Gödel Drug Discovery Machine (DGDM): A Self-Improving AI Framework

bioRxiv Jan 1, 2025
Despite advances such as AlphaFold and modern generative AI models, current drug discovery pipelines lack mechanisms to refine both molecules and the pipelines themselves, limiting their ability to achieve autonomous and reliable self-improvement. To...
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CNN-based learning of single-cell transcriptomes reveals a blood-detectable multi-cancer signature of brain metastasis

bioRxiv Jan 1, 2025
Brain metastasis (BrM) is a serious complication of advanced cancers and remains difficult to predict before clinical symptoms appear. To investigate shared transcriptional features of BrM across tumour types, we integrated single-cell RNA sequencing...
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CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target Affinity

bioRxiv Jan 1, 2025
Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of accessible protein structure prediction methods such as AlphaFold, predicted protein 3D structures are...
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Canonical Representational Mapping for Cognitive Neuroscience

bioRxiv Jan 1, 2025
Understanding neural representations is central to cognitive neuroscience, yet isolating meaningful patterns from noisy or correlated data remains challenging. Canonical Representational Mapping (CRM) is a novel multivariate analysis method to identi...
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