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Crohn Disease

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Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset.

PloS one
Metabolomic data processing pipelines have been improving in recent years, allowing for greater feature extraction and identification. Lately, machine learning and robust statistical techniques to control false discoveries are being incorporated into...

Brief Survey on Machine Learning in Epistasis.

Methods in molecular biology (Clifton, N.J.)
In biology, the term "epistasis" indicates the effect of the interaction of a gene with another gene. A gene can interact with an independently sorted gene, located far away on the chromosome or on an entirely different chromosome, and this interacti...

Developing a Neural Network Model for a Non-invasive Prediction of Histologic Activity in Inflammatory Bowel Diseases.

The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology
BACKGROUND: Colonoscopy with biopsy is the "gold" standard for evaluating disease activity in inflammatory bowel diseases (IBD). Current research is geared toward finding non-invasive, cost-efficient methods that estimate disease activity. We aimed t...

Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox.

Genome biology
The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing i...

Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency department.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Patients with Crohn's disease (CD) frequently undergo abdominopelvic computed tomography (APCT) in the emergency department (ED). It's essential to diagnose clinically actionable findings (CAF) as they may need immediate intervention, fre...

The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease.

Inflammatory bowel diseases
BACKGROUND: Although imaging, endoscopy, and inflammatory biomarkers are associated with future Crohn disease (CD) outcomes, common laboratory studies may also provide prognostic opportunities. We evaluated machine learning models incorporating routi...

Deep learning conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study.

World journal of gastroenterology
BACKGROUND: Traditional methods of developing predictive models in inflammatory bowel diseases (IBD) rely on using statistical regression approaches to deriving clinical scores such as the Crohn's disease (CD) activity index. However, traditional app...

Non-invasive diagnosis of Crohn's disease based on SERS combined with PCA-SVM.

Analytical methods : advancing methods and applications
Crohn's disease (CD) is an idiopathic chronic inflammatory bowel disease without a cure. Most of the CD patients are firstly diagnosed by invasive endoscopy, and clinical and pathological examinations are further required to confirm the diagnosis. He...

Identification of Ulcers and Erosions by the Novel Pillcamâ„¢ Crohn's Capsule Using a Convolutional Neural Network: A Multicentre Pilot Study.

Journal of Crohn's & colitis
BACKGROUND AND AIMS: Capsule endoscopy is a central element in the management of patients with suspected or known Crohn's disease. In 2017, PillCamâ„¢ Crohn's Capsule was introduced and demonstrated to have greater accuracy in the evaluation of extensi...

Differentiation of intestinal tuberculosis and Crohn's disease through an explainable machine learning method.

Scientific reports
Differentiation between Crohn's disease and intestinal tuberculosis is difficult but crucial for medical decisions. This study aims to develop an effective framework to distinguish these two diseases through an explainable machine learning (ML) model...