Artificial Intelligence Medical Compendium

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

Showing 561 to 570 of 213,137 articles

Deep learning representations of human Immune Health for precision immunology

bioRxiv Jul 20, 2026
The human immune system is composed of [~]30-50 distinct cell types, each of which can exist in different states of activation or differentiation. Indeed, the mammalian immune system has evolved to sense and respond to infections, cancers, injuries, ... read more 
immunology

scRepresenter: a workflow for computing, integrating and benchmarking cellular representations in single-cell transcriptomics

bioRxiv Jul 20, 2026
Motivation: Single-cell RNA sequencing (scRNA-seq) has become an attractive tool for studying complex diseases, in which transient cell states affecting diverse cell populations characterise disease development and progression. However, due to data s... read more 
bioinformatics

Leveraging multiplicity in biologically informed neural networks to uncover disease heterogeneity

bioRxiv Jul 20, 2026
Biologically inspired neural networks (BINNs) embed pathway, ontology, or protein-interaction structure directly into neural networks, promising interpretable disease prediction where hidden nodes map to named biological entities. Yet BINNs have been... read more 
bioinformatics

ProteinDock: A physics-informed layer to improve protein-protein docking reliability

bioRxiv Jul 20, 2026
Computational modeling provides geometric insight into protein-protein interactions without requiring the resources of experimentation. However, reliability can be hindered when modeling proteins with distinctive features, such as antibodies, that us... read more 
bioengineering

Neocortical astrocyte diversity stems from distinct developmental origins

bioRxiv Jul 20, 2026
Key regulators of neural network activity in multiple advanced cognitive processes and essential components of the blood-brain barrier, astrocytes constitute a highly heterogeneous population at the morphological, molecular, and functional levels. Ho... read more 
neuroscience

Diversity and evolution of the transcriptional regulatory networks of Pseudomonas strains revealed using machine learning

bioRxiv Jul 20, 2026
The genus Pseudomonas consists of diverse and ecologically significant species that form close associations with both plants and animals. This genus is widely studied due to the clinically relevant Pseudomonas aeruginosa, model plant pathogen Pseudom... read more 
systems biology

Natural language processing tool for extracting information about opioid overdoses in the USA from case narratives in the violent death reporting system.

Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention Jul 20, 2026
BACKGROUND: Improving the infrastructure for drug overdose surveillance is critical for identifying new threats and responding to emerging trends. We aimed to develop a prototype tool using the principles of natural language processing that can extra... read more 

Calibrated Alzheimer's Conversion Risk in Mild Cognitive Impairment: Persistent Homology of Clinical Trajectories with Conformal Guarantees

arXiv Jul 20, 2026
Background. Predicting conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is central to trial enrichment and care planning, yet existing models provide no individual-level uncertainty estimates and rarely include transparent ... read more 
Machine Learning stat.ML

Bio-SFT: Asymmetric Cortical Guidance and Retinal Adaptation for Robust HDR Reconstruction

arXiv Jul 20, 2026
Recovering high dynamic range (HDR) radiance from a single standard dynamic range (SDR) image is highly ill-posed. Extreme luminance variation and severe quantization in dark regions make accurate reconstruction challenging, often leading to visual a... read more 
Computer Vision and Pattern Recognition

DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation

arXiv Jul 20, 2026
Few-shot Test-Time Domain Adaptation (FSTT-DA) seeks to adapt models to novel domains using only a handful of unlabeled target samples. This setting is more realistic than typical domain adaptation setups, which assume access to target data during so... read more 
Computer Vision and Pattern Recognition cs.LG
Popular Topics
Recent Journals