AIMC Journal:
bioRxiv

Showing 321 to 330 of 4935 articles

Macrophage signature-based prediction of cancer treatment response using MIL-attention

bioRxiv Sep 3, 2026
Predicting immunotherapy response from single-cell data remains difficult due to patient-level labels, extreme class imbalance, and highly heterogeneous macrophage states. We present a Multiple Instance Learning (MIL) framework that treats each patie...
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Beyond benchmark accuracy: machine-learning turnover-number predictors require system-level validation

bioRxiv Sep 3, 2026
Enzyme turnover numbers (kcat) are essential for kinetic models and enzyme-constrained genome-scale metabolic models (ecGEMs), but measured values are sparse and therefore increasingly estimated using machine learning (ML). Although these predictors ...
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Characterizing the landscape of gene process dependencies in cancer

bioRxiv Sep 2, 2026
Precision oncology aims to tailor cancer treatment to tumor genetics but it currently benefits only a small fraction of patients, in part because the primary focus is to match drug targets to single genes. The Cancer Dependency Map Project (DepMap) a...
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High-Resolution Subtyping of Pediatric Low-Grade Glioma Using an Integrated Meta-Clustering Framework

bioRxiv Sep 2, 2026
Pediatric low-grade glioma (pLGG) is the most common type of brain tumor in children, accounting for approximately 30% of all central nervous system tumors in children. pLGG has multiple molecular subtypes that differ in disease progression, recurren...
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Accessible and reproducible deployment reveals the practical boundaries of single-cell foundation models

bioRxiv Sep 2, 2026
Single-cell foundation models (scFMs) have been widely promoted as a unifying paradigm for transcriptomic analysis, yet whether large-scale pretraining translates into reproducible biological advantages remains unclear. Their adoption is further hind...
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The first OpenBind release: An open experimental structure-affinity dataset and benchmark for structure-based AI

bioRxiv Sep 1, 2026
High-quality experimental datasets that link protein-ligand structures with binding affinity data are essential for developing and evaluating structure-based machine learning methods. To help address this need, we established OpenBind as an open-scie...
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GNMCADS: Sampling For Protein Conformation Diversity With Gaussian Network Model Guided Condition Annealed Diffusion Sampler

bioRxiv Sep 1, 2026
Proteins are dynamic molecules existing in diverse conformational states underlying their biological functions. Although recent approaches have enabled diverse conformational sampling by emulating molecular dynamics simulations, perturbing evolutiona...
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Accurate detection of metagenomic strain-level associations using average nucleotide identity with StrainSpy

bioRxiv Sep 1, 2026
Genetic variation among microbial strains of the same species can profoundly influence their phenotypes, ecological functions, and impacts on human health. Traditionally, the relative abundance of a species has been used to identify associations betw...
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Learning and forecasting shared evolutionary pathways to multi-drug resistance across global pathogens

bioRxiv Sep 1, 2026
Infections with bacteria which have evolved multi-drug resistance (MDR) cause millions of deaths worldwide. Large-scale efforts are gathering genotypic and phenotypic data on MDR bacteria, but methods for learning the structure, diversity, and predic...
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Detection of Frustration-related Operant Behavior in Rats via Machine Learning Methods

bioRxiv Sep 1, 2026
Despite its strong link to neuropsychiatric conditions, frustration remains critically understudied in humans and animals alike. Therefore, there is an urgent need to develop tools to understand and therapeutically target frustration-related function...
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