Artificial Intelligence Medical Compendium

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

Showing 27,661 to 27,670 of 218,939 articles

Prediction model for daily feed intake during the growing period in floor-reared Wanxi White Geese based on machine learning and feeding behavior features.

Poultry science Apr 23, 2026
To improve the efficiency of obtaining daily feed intake (DFI) of Wanxi White geese under floor-rearing conditions, this study used 200 Wanxi White geese as experimental animals. Individual information such as body weight (BW) and feeding behavior da... read more 

Three-dimensional oxygen maps of tumors in real time - Analysis in the context of active tumor vasculature.

Computer methods and programs in biomedicine Apr 23, 2026
BACKGROUND AND OBJECTIVE: Characterizing the tumor microenvironment (TME) requires integrating multiple physiological features, including oxygenation, vascularity, and redox status. While EPR oxygen imaging (EPROI) provides spatial pO₂ maps, conventi... read more 

Depth map filtering method for shape-from-focus recovery based on hybrid network model.

Micron (Oxford, England : 1993) Apr 23, 2026
Shape-from-focus (SFF) is an economically efficient 3D shape recovery technology. As a core module of 3D digital microscopes, its primary goal is to acquire high-quality depth maps. However, the performance of such technology is highly dependent on t... read more 

Reveal Principles of Codon Optimization via Machine Learning

bioRxiv Apr 23, 2026
High level of protein expression is usually welcomed in industry and research, and codon optimization is widely used to achieve high expression. Methods of implementing codon optimization can be divided into two branches, one is classical methods whi... read more 
bioinformatics

Autonomous multimodal agents enable transparent, spatiotemporal reconstruction of immune dynamics in pancreatic cancer progression

bioRxiv Apr 23, 2026
Pancreatic cancer progression is orchestrated by dynamic shifts in immune and stromal cellular ecosystems, yet the temporal and spatial principles governing these transitions remain poorly understood. Here, we present an agentic computational patholo... read more 
cancer biology

A transcriptomic analysis reveals shared and inducer-specific expression patterns of cellular senescence

bioRxiv Apr 23, 2026
Cellular senescence is a heterogeneous cell state induced by diverse stressors, including telomere attrition, genotoxic agents, oxidative damage, and inflammation. Despite ongoing efforts to identify conserved senescence biomarkers, it remains unclea... read more 
genomics

Interpreting and Validating a Deep Learning Model Predictive of Spatial Morphologic-Molecular Patterns in Lung Adenocarcinoma, Using Ground Truth Immunohistochemistry

bioRxiv Apr 23, 2026
Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer, exhibits profound histological and molecular heterogeneity. While genomic profiling has identified key oncogenic drivers and immune signatures, its use is limited by c... read more 
pathology

Lightning Pose 3D: an uncertainty-aware framework for data-efficient multi-view animal pose estimation

bioRxiv Apr 23, 2026
Multi-view pose estimation is essential for quantifying animal behavior in scientific research, yet current methods struggle to achieve accurate tracking with limited labeled data and suffer from poor uncertainty estimates. We address these challenge... read more 
neuroscience

Large Language Model Agent Enables Autonomous Machine Learning Model Building for Biomedicine

bioRxiv Apr 23, 2026
Machine learning accelerates biomedical discovery, but creating effective predictive models requires specialized human expertise and demanding manual effort. Researchers must iteratively design pipelines, select architectures, and debug code. This ch... read more 
bioinformatics

An Embeddings Fusion Approach Predicts Disease State from Microbiome Features

bioRxiv Apr 23, 2026
Background Deep neural networks are a proven technique for working with high dimensional data because of their ability to draw-out meaningful patterns and create vector representations known as embeddings, which make it easier to work with learning t... read more 
bioinformatics
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