AIMC Journal:
bioRxiv

Showing 1861 to 1870 of 4956 articles

Explainable machine learning identifies candidate shared neuroanatomical features in Alzheimer's and Parkinson's via importance inversion transfer

bioRxiv Apr 14, 2026
Despite significant neurobiological and pathological overlaps, Alzheimer's (AD) and Parkinson's (PD)-the primary threats to healthy aging-are still managed as distinct clinical entities. Standard machine learning exacerbates this fragmentation by pri...
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A Scalable High-Density Microwell Assay for Single-Cell Clonal Expansion Profiling

bioRxiv Apr 14, 2026
Traditional clonogenic assays remain central to evaluating the self-renewal capacity of tumor cells. However, the assay relies on subjective endpoint measurements, is restricted to two-dimensional monolayer growth, and lacks the single cell resolutio...
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Reconstructing intra-tumor fitness landscapes from scSeq CNA genotypes via simulation-based Bayesian inference and Deep Learning

bioRxiv Apr 14, 2026
Inferring the selective effects of copy-number alterations (CNAs) from clonal tumor data is essential for understanding tumor evolution. In practice, intra-tumor evolutionary parameters are typically estimated by fitting population genetic models to ...
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How Generative Models Approach Molecular Conformational Sampling

bioRxiv Apr 14, 2026
Characterising equilibrium conformational ensembles with deep generative models requires assessing not only whether a model reproduces the target distribution, but also the mechanism of how it arrives here. Here we examine two distinct routes to gene...
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Multi-Stain Fusion of Histopathology Images Using Deep Learning for Pediatric Brain Tumor Classification

bioRxiv Apr 14, 2026
The classification of pediatric brain tumors is investigated using deep learning on hematoxylin and eosin (H&E) and antigen Ki-67 (Ki-67) whole slide images (WSIs) from the Children's Brain Tumor Network (CBTN) dataset. A total of 1,662 unregistered ...
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A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis

bioRxiv Apr 14, 2026
Current single-cell foundation models rely on language-model architectures that ignore transcriptomic data distributions, often underperforming specialized methods. We introduce xVERSE, a transcriptomics-native foundation model coupling batch-invaria...
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Harnessing AI to Build Virtual Cells

bioRxiv Apr 14, 2026
A virtual cell is a world model of a cell: a computational system that predicts, simulates and programs cellular processes across modalities and scales. An important path toward this goal is to model how genetic and chemical perturbations give rise t...
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TB-Bench: A Systematic Benchmark of Machine Learning and Deep Learning Methods for Second-Line TB Drug Resistance Prediction

bioRxiv Apr 13, 2026
Drug-resistant tuberculosis (TB), characterized by prolonged treatment regimens and suboptimal treatment outcomes, remains a major obstacle to global TB elimination. Advances in sequencing technologies have enabled the development of machine-learning...
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In-silico cell sorting revealed granulocyte-specific single-cell-type gene expression from peripheral blood bulk expression data and its application as host response biomarkers to discriminate bacterial and viral infections

bioRxiv Apr 13, 2026
Peripheral Blood transcriptome analysis evaluated the bulk transcript abundance (TA) covering all leukocyte cell populations. However, there are 2 main problems in using bulk expression as biomarkers: (1) A long list of differential expression genes ...
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Identification and Analysis of Novel RNA Editing Sites in Neurodegenerative Diseases Using Machine Learning Approaches.

bioRxiv Apr 13, 2026
ABSTRACT Background: RNA editing is a post-transcriptional modification that alters the sequence of an RNA transcript. Two types of RNA editing were found in mammals, involving the enzymatic deamination of either adenosine to inosine (A-to-I) or cyti...
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