Artificial Intelligence Medical Compendium

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

Showing 3,081 to 3,090 of 202,937 articles

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 

Convergent genome- and gene-level constraints shape repeated environmental adaptation in grasses

bioRxiv
Grasses (Poaceae) dominate terrestrial ecosystems and sustain global food security, yet the genomic principles enabling their repeated adaptation to extreme environments remain unresolved. Combining dense phylogenomic sampling, global environmental d... read more 

Spatiotemporal Decoding of Explore-Exploit Decisions in the Human Brain

bioRxiv
Adaptive behavior requires flexibly shifting between exploiting familiar rewards and exploring novel opportunities. These explore-exploit decisions are implemented via a distributed brain network, anchored in frontopolar cortex (FPC) and ventromedial... read more 

Data-Efficient Exploration of Enzyme Function Using Family-Specific Machine Learning

bioRxiv
Enzymes are essential biocatalysts across diverse industries, driving demand for high-performing variants. Foundation models are attractive for guiding enzyme discovery, but often lack the resolution to model subtle variations driving function within... read more