Artificial Intelligence Medical Compendium

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

Showing 64,471 to 64,480 of 231,605 articles

Beyond native sequence recovery: Improved modeling of thesequence-energy landscape of protein structures

bioRxiv
Computational protein design using machine learning models has advanced rapidly since the introduction of AlphaFold2. There is now a suite of tools that enable in silico design of proteins with desired structures and properties. Most design workflows... read more 

Leveraging Pretrained Vision Transformers for classifying Alcohol Use Disorder using Raw Resting-State EEG

bioRxiv
Alcohol Use Disorder (AUD) is a prevalent and debilitating neuropsychiatric condition characterized by compulsive alcohol consumption, impaired control, and negative emotional states, affecting about 28 million adults in the United States. Despite it... read more 

The geometry of context-dependent biased decisions during learning

bioRxiv
Adaptive behavior requires inferring latent context and rapidly adjusting decisions in response to changing environmental contingencies. We investigated how reward context is learned, represented, and updated during decision making. We recorded large... read more 

High-dimensional spatial proteomics and novel machine learning pipeline identifies disease specific renal damage states

bioRxiv
Lupus nephritis (LuN) and renal allograft rejection (RAR) manifest inflammation and fibrosis that ultimately lead to kidney failure. To quantitatively assess spatial injury patterns, we collected high dimensional spatial proteomics data from 23 LuN, ... read more 

Extending Conformational Ensemble Prediction to Multidomain Proteins and Protein Complex

bioRxiv
Proteins execute cellular functions through a structural continuum ranging from stable, folded domains to highly dynamic intrinsically disordered regions (IDRs). Conformational ensembles represent the set of three-dimensional structures a protein ado... read more 

Label-free detection of individual virus-infected cells using deep learning

bioRxiv
Numerous applications in research and medicine rely on reliable identification and quantification of virus-infected cells. Current methods either apply reporter viruses, that often differ from clinical isolates (e. g. cell tropism, immune evasion) or... read more 

MorphoLearn: A morphology-driven workflow to decipher 3D electron microscopy segmentation in diatoms

bioRxiv
Three-dimensional electron microscopy (3D EM) enables the quantitative analysis of cellular ultrastructure. However, large-scale segmentation of whole-cell volumes poses a significant challenge, especially in biologically diverse systems. Unlike medi... read more 

Deployable high-fidelity metagenome binning at scale with QuickBin

bioRxiv
1Reconstructing genomes from metagenomic assemblies is foundational to microbiome research, yet metagenome binning remains constrained by a persistent trade-off between genome fidelity and deployable throughput. Many high-accuracy approaches rely on ... read more 

ATN Classification and Machine-Learned Plasma Biomarker Phenotypes Reveal Distinct Alzheimer's Pathology in a Population-Based Cohort

medRxiv
BackgroundThe ATN (Amyloid/Tau/Neurodegeneration) framework provides a theory-driven approach to Alzheimers disease (AD) classification using binary biomarker cutoffs, while unsupervised machine learning offers data-driven phenotyping. The concordanc... read more 

Onco-Seg: Adapting Promptable Concept Segmentation for Multi-Modal Medical Imaging

medRxiv
Medical image segmentation remains a critical bottleneck in clinical workflows, from diagnostic radiology to radiation oncology treatment planning. We present Onco-Seg, a medical imaging adaptation of Metas Segment Anything Model 3 (SAM3) that levera... read more