Machine Learning to Predict Anti–Tumor Necrosis Factor Drug ...

Machine learning models with time-series clinical features to predict ...

Machine learning in medicine. ... Impact of anti-tumor necrosis factor treatment on lipid profiles in Korean patients with ankylosing spondylitis.

Functional coding haplotypes and machine-learning feature ...

Guan, Y ∙ Zhang, H ∙ Quang, D ∙ et al. Machine learning to predict anti–tumor necrosis factor drug responses of rheumatoid arthritis patients by integrating ...

Long-term clinical and real-world experience with Crohn's disease ...

Although anti-tumor necrosis factor (TNF)-α agents are important therapeutic drugs for Crohn's disease (CD), data regarding their long-term sustained effects ...

Identification of a Rule to Predict Response to Sarilumab in Patients ...

Guan Y, Zhang H, Quang D, et al. Machine learning to predict anti-tumor necrosis factor drug responses of rheumatoid arthritis patients by ...

Novel approaches to develop biomarkers predicting treatment ...

Machine Learning to Predict Anti-Tumor Necrosis. Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and. Genetic ...

Prediction of Relapse After Anti-Tumor Necrosis Factor Cessation in ...

3 Despite the expanding arsenal of medication options in CD, the use of anti-TNF therapy may increase further with the introduc- tion of biosimilars and with ...

Integrative Clinical, Molecular, and Computational Analysis Identify ...

Machine learning to predict anti–tumor necrosis factor drug responses of rheumatoid arthritis patients by integrating clinical and genetic.

Deep learning vs conventional learning algorithms for clinical ...

This study has exhibited the potential of deep learning algorithms in predicting response to anti-TNF therapy in patients with CD. The ability ...

Artificial intelligence for predicting treatment responses in ... - Frontiers

Risk factor identification and modeling by machine learning. Tumor necrosis factor inhibitors (TNFis) are commonly utilized in treating ...

Unlocking RA treatment responses with predictive protein profiles

Plasma from 144 RA patients on anti-TNF therapy interrogated with four Olink Target 96 panels. · Baseline samples examined by PCA analysis – machine learning ...

World Journal of - Gastroenterology - NET

the utility of machine learning in predicting anti-TNF ... Learning to Predict Anti-Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis ...

Mucosal microbiota and gene expression are associated with long ...

The advent of anti-tumor necrosis factor (anti-TNF) agents has revolutionized the treatment of inflammatory bowel disease (IBD), which comprises ...

Tumor necrosis factor-related lncRNAs predict prognosis and ...

The tumor necrosis factor (TNF) family is involved in tumorigenesis and tumor progression. Various long non-coding RNAs (lncRNAs) play important ...

Research Conducted at Microsoft Corporation Has Provided ... - Gale

Research Conducted at Microsoft Corporation Has Provided New Information about Rheumatoid Arthritis (Machine Learning To Predict Anti-tumor ...

Multiomics and Machine Learning Accurately Predict Clinical ...

To predict response to anti-tumor necrosis factor (anti-TNF) prior to treatment in patients with rheumatoid arthritis (RA), and to comprehensively ...

Machine learning-based prediction of rheumatoid arthritis ... - PLOS

Machine learning to predict anti-tumor necrosis factor drug responses of rheumatoid arthritis patients by integrating clinical and genetic ...

Prediction of Persistence to Treatment for Patients with Rheumatoid ...

Machine Learning to Predict Anti-Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers ...

machine learning to predict early tnf inhibitor users in ... - sparx-ip.net

Machine learning models were formulated to predict the early-TNF users using the baseline data. Additionally, feature importance analysis was performed to ...

Predictors of tumor necrosis factor inhibitors primary failure in ...

of patients does not give a response despite therapy. It remains a challenge to predict which patients will respond. Our study aims to ...

A network-based framework to discover treatment-response ...

With ProBeNet, biomarkers were discovered predicting patient responses to both an established autoimmune therapy (infliximab) and an ...