Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 5,911 to 5,920 of 205,623 articles

The machine-learning classifier ALLCatchR2 identifies 20 T-ALL subtypes across cohorts and age groups

bioRxiv
T-cell acute lymphoblastic leukemia (T-ALL) comprises molecularly diverse subtypes, but robust cross-cohort validations and operational gene-expression definitions are lacking. To establish a gene-expression-anchored framework for T-ALL subtyping, we... read more 

CodeCytos: AI-assisted spatial molecular imaging analysis via code-augmented agent action space

bioRxiv
Conventional tissue image analysis software provides foundational capabilities for cellular analysis, including segmentation, morphological feature extraction, and spatial organization analysis; however, these tools often require manual intervention ... read more 

SciCore-Omics: a tri-modal foundation model unifying histology, spatial transcriptomics and language for spatial biology

bioRxiv
Histomorphology and spatial transcriptomics capture complementary aspects of tissue biology, but their relationships remain difficult to extract, align, and interpret at scale. Existing foundation models typically connect histology, omics, or languag... read more 

AdventML: Advanced Enzyme Temperature Prediction with Transformer-Based Embeddings and Resampling Strategies

bioRxiv
Accurate prediction of enzymes' optimal catalytic temperature (Topt) is crucial in biotechnology, as enzymes with extreme Topt values are highly desirable for reactions at extreme temperatures and for their general stability. However, experimental de... read more 

HyperNiche: Learning Heterophilic Cellular Niches with Hypergraph Neural Networks

bioRxiv
We propose HyperNiche, a hypergraph-based framework for modeling higher-order, heterogeneous cellular niches from spatial transcriptomics data. Unlike conventional graph-based methods that rely on pairwise similarity and tend to produce homogeneous c... read more 

sstar2: A Python Package for S*-based Archaic Introgression Detection with Machine Learning

bioRxiv
Detecting introgressed genomic fragments from unsampled or extinct source populations remains challenging. The S* statistic is widely used for this purpose, but the original sstar implementation relies on generalized additive models to smooth quantil... read more 

MAGI: Mechanistic Consequences of Genetic Variants via Genomic Foundation Models

bioRxiv
Clinical variant interpretation requires mechanism-aware evidence to guide diagnosis and clarify the biological consequences of mutations. However, existing computational predictors and genomic foundation models largely function as black boxes, provi... read more 

ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures

bioRxiv
Deep learning-based protein structure prediction methods have led to a paradigm-shift in computational structural biology, yet reliably assessing the quality of computationally predicted multimeric structures remains challenging. Recent methods have ... read more 

ViTAMIn-O: Democratizing computer vision-based machine learning for stem cell research

bioRxiv
Deep Learning (DL) holds exciting potential in automating the prediction of organoid differentiation results. Nevertheless, current models lack adaptability, openness, and robustness in performance. Additionally, broad employments of predictive model... read more 

Integrating Histology with Spatial Molecular Programs Using a Multimodal Foundation Model

bioRxiv
Histopathological assessment remains central to cancer diagnosis and stratification, yet its mechanistic interpretation remains limited without molecular context. To address this, we developed SQUALL, a multimodal foundation model integrating histolo... read more