Rheumatology

Reply to "A deep learning-based smartphone platform for cutaneous lupus erythematosus classification assistance - simplifying the diagnosis of complicated diseases": Machine learning evaluations need to consider the true clinical context.

Opportunities and Challenges in Democratizing Immunology Datasets.

The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniques have presented opportunities to explore untapped areas of immunological research. Broad initiatives are launched to disseminate the datasets siloed...

Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors.

A generative adversarial autoencoder for the rational design of potential HIV-1 entry inhibitors able to block CD4-binding site of the viral envelope ...

Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs.

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to tre...

Machine Learning for Prediction and Risk Stratification of Lupus Nephritis Renal Flare.

BACKGROUND: Renal flare of lupus nephritis (LN) is strongly associated with poor kidney outcomes, and predicting renal flare and stratifying its risk ...

Deep Learning Classification of Systemic Sclerosis Skin Using the MobileNetV2 Model.

Systemic sclerosis (SSc) is a rare autoimmune, systemic disease with prominent fibrosis of skin and internal organs. Early diagnosis of the disease i...

Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls.

Celiac disease (CeD) is a common autoimmune disorder caused by an abnormal immune response to dietary gluten proteins. The disease has high heritabili...

Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases.

In the context of the recently emerging COVID-19 pandemic, we developed a deep learning model that can be used to predict the inhibitory activity of 3...

Is Anti-Müllerian Hormone a Marker of Ovarian Reserve in Young Breast Cancer Patients Receiving a GnRH Analog during Chemotherapy?

INTRODUCTION: Anti-Müllerian hormone (AMH) is the most reliable biomarker of ovarian reserve; however, its role in predicting ovarian recovery after c...

Application Prospect of Artificial Intelligence in Rehabilitation and Management of Myasthenia Gravis.

Myasthenia gravis (MG) is a chronic autoimmune disease of the nervous system, which is still incurable. In recent years, with the progress of immunosu...

SMORE: A Self-Supervised Anti-Aliasing and Super-Resolution Algorithm for MRI Using Deep Learning.

High resolution magnetic resonance (MR) images are desired in many clinical and research applications. Acquiring such images with high signal-to-noise...

Protein glycation - biomarkers of metabolic dysfunction and early-stage decline in health in the era of precision medicine.

Protein glycation provides a biomarker in widespread clinical use, glycated hemoglobin HbA (A1C). It is a biomarker for diagnosis of diabetes and pred...

A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning.

In computer-aided diagnosis (CAD) systems, the automatic classification of the different types of the human epithelial type 2 (HEp-2) cells represents...

A deep learning-based smartphone platform for cutaneous lupus erythematosus classification assistance: Simplifying the diagnosis of complicated diseases.

Prioritizing Pain-Associated Targets with Machine Learning.

While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are l...

Coupled liquid crystalline oscillators in Huygens' synchrony.

In the flourishing field of soft robotics, strategies to embody communication and collective motion are scarce. Here we report the synchronized oscill...

TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.

Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-gr...

Antibody Supervised Training of a Deep Learning Based Algorithm for Leukocyte Segmentation in Papillary Thyroid Carcinoma.

The quantity of leukocytes in papillary thyroid carcinoma (PTC) potentially have prognostic and treatment predictive value. Here, we propose a novel m...

Predicting Incremental and Future Visual Change in Neovascular Age-Related Macular Degeneration Using Deep Learning.

PURPOSE: To evaluate the predictive usefulness of quantitative imaging biomarkers, acquired automatically from OCT scans, of cross-sectional and futur...

Identification of drug combinations on the basis of machine learning to maximize anti-aging effects.

Aging is a multifactorial process that involves numerous genetic changes, so identifying anti-aging agents is quite challenging. Age-associated geneti...