Rheumatology

Latest AI and machine learning research in rheumatology for healthcare professionals.

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Accuracy and Scalability of Machine Learning Methods for Genotype-Phenotype Association Data

Many machine learning methods can be applied to predicting phenotypes from genetic data. Which of these methods work best remains an open question, however. To answer this question, we propose to compare a variety of approaches’ ability to predict a simulated non-linear complex trait. Specifically, we evaluate these methods on their accuracy and scalability with respect to the amount training data...

Screening of Oyster Peptides for Anti-Muscle Atrophy Based on Machine Learning and Computer Simulation: Guided by Antioxidant Pathways

Muscle atrophy poses a serious threat to human health, with its primary pathogenic mechanisms closely linked to oxidative stress. This study focuses on the potential of oyster peptides in alleviating dexamethasone (DEX)-induced skeletal muscle atrophy and their underlying antioxidant mechanisms. Utilizing efficient integrated machine learning and computer simulation methods, a systematic screening...

Integrated analysis implicates novel insights of NMB into lactate metabolism and immune response prediction in primary glioblastoma

Glioblastoma (GBM), the most aggressive primary brain tumor in adults, exhibits profound treatment resistance and poor prognosis. Despite advances in ...

A validated set of neural gene reporter mice and chemical tracers tools for mapping knee innervating neurons

Joint pain is an increasing concern for our aging population, as current therapies to slow joint disease progression or reduce pain are largely ineffe...

Cellohood: multi-granular discovery of cellular neighborhoods with a permutation-invariant set transformer auto-encoder

Discovering cellular neighborhoods and their roles in disease requires computational methods that consider the full breadth of data and offer multi-le...

Targeting peptide–MHC complexes with designed T cell receptors and antibodies

Class I major histocompatibility complexes (MHCs), expressed on the surface of all nucleated cells, present peptides derived from intracellular protei...

GenVS-TBDB: A Target-Aware AI-Generated and Virtual-Screened Small-Molecule Library for Tuberculosis Drug Discovery

Tuberculosis (TB) remains a leading global health threat, with over 10 million new cases and 1.25 million deaths reported in 2023. Current TB therapie...

The FERM Guild: A Differentially Correlated Microbial Module Drives Hypertension via Metabolic Flux Perturbations

Hypertension is a major risk factor for cardiovascular diseases, with changes in gut microbiota composition and function being closely associated with...

Ensemble-DeepSets: an interpretable deep learning framework for single-cell resolution profiling of immunological aging

Immunological aging (immunosenescence) drives increased susceptibility to infections and reduced vaccine efficacy in elderly populations. Current bulk...

Deep learning inference of universal dormancy pseudotime reveals the cellular targets of anti-cancer therapies

Controlled exit from and re-entry into the cell cycle is essential for multi-cellular life, while aberrant quiescent and senescent cell states have be...

Exploiting pair correlation function to describe biological tissue structure

Multiplexed imaging technologies now enable the simultaneous profiling of hundreds to thousands of molecular targets in intact tissues, providing unpr...

Modeling and Design of Multi-layered Cylindrical Microcapsules for Intravitreal Controlled Release

Chronic diseases often require repeated oral or local administration, which can compromise patient compliance. In wet age-related macular degeneration...

Leveraging Hand-Crafted Radiomics on Multicenter FLAIR MRI for Predicting Disability Progression in People with Multiple Sclerosis

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that results in varying degrees of functional impairment. Conven...

Retinal vascularization rate predicts retinopathy of prematurity and remains unaffected by low-dose bevacizumab treatment

To assess the rate of retinal vascularisation derived from ultra-widefield (UWF) imaging-based retinopathy of prematurity (ROP) screening as predictor...

A micro-ChromaDot array with AI integration for the detection of multiple biomarkers in a small portable device

With the rapid growth of digital healthcare, diagnosis, prognosis, and monitoring of chronic and acute diseases at home are increasingly in demand. In...

Development of an artificial intelligence-generated, explainable treatment recommendation system for urothelial carcinoma and renal cell carcinoma to support multidisciplinary cancer conferences

Decisions on the best available treatment in clinical oncology are based on expert opinions in multidisciplinary cancer conferences (MCC). Artificial ...

Automated Detection of Faciobrachial Dystonic Seizures Related Events in LGI1 Autoimmune Encephalitis Patients with Wearables

To evaluate the potential of wrist-worn wearable devices to detect and quantify Faciobrachial Dystonic Seizures (FBDS) and related events associated w...

irAE-GPT: Leveraging large language models to identify immune-related adverse events in electronic health records and clinical trial datasets

Large language models (LLMs) have emerged as transformative technologies, revolutionizing natural language understanding and generation across various...

Machine Learning for Predicting Thrombotic Recurrence in Antiphospholipid Syndrome

Thrombotic Antiphospholipid Syndrome (TAPS) is an autoimmune disorder associated with a high risk of recurrent thromboembolic events. Despite advances...

Proteome-wide autoantibody screening and holistic autoantigenomic analysis unveil COVID-19 signature of autoantibody landscape

This study presents “aUToAntiBody Comprehensive Database (UT-ABCD)”, a comprehensive catalog of autoantibody profiles in 284 human individuals. The su...

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