Rheumatology

Rheumatoid Arthritis

Latest AI and machine learning research in rheumatoid arthritis for healthcare professionals.

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Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies.

To develop a machine learning (ML) model that predicts disease groups or autoantibodies in patients ...

Finite-time control of delay switched systems via input anti-bump switching.

This article devotes to the finite-time (FT) input anti-bump switching control (SC) issue for a kind...

A Zwitterionic-Aromatic Motif-Based ionic skin for highly biocompatible and Glucose-Responsive sensor.

Electronic skins that can sense external stimuli have been of great significance in artificial intel...

A computational method for drug sensitivity prediction of cancer cell lines based on various molecular information.

Determining sensitive drugs for a patient is one of the most critical problems in precision medicine...

Artificial Intelligence-Assisted Amphiregulin and Epiregulin IHC Predicts Panitumumab Benefit in Wild-Type Metastatic Colorectal Cancer.

PURPOSE: High tumor mRNA levels of the EGFR ligands amphiregulin (AREG) and epiregulin (EREG) are as...

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 abl...

Analysis of Tumor Microenvironment Characteristics in Bladder Cancer: Implications for Immune Checkpoint Inhibitor Therapy.

The tumor microenvironment (TME) plays a crucial role in cancer progression and recent evidence has ...

antioxidant and anti-inflammatory activities of ethanol stem-bark extract of K.D. Koenig.

() K.D. Koenig (Family Sapindaceae) is a branchless straight bole approximately 15 m in length. The...

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 accuratel...

Antimicrobial Activity of Necklace Orchids is Phylogenetically Clustered and can be Predicted With a Biological Response Method.

Necklace orchids (Coelogyninae, Epidendroideae) have been used in traditional medicine practices for...

Target2DeNovoDrug: a novel programmatic tool for -deep learning based drug design for any target of interest.

The on-going data-science and Artificial Intelligence (AI) revolution offer researchers a fresh set ...

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 c...

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; howeve...

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 application...

Coupled liquid crystalline oscillators in Huygens' synchrony.

In the flourishing field of soft robotics, strategies to embody communication and collective motion ...

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 resist...

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 trea...

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 automati...

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 ...

Anti-senescent drug screening by deep learning-based morphology senescence scoring.

Advances in deep learning technology have enabled complex task solutions. The accuracy of image clas...

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