ArticleScientific reports2020
Detecting ulcerative colitis from colon samples using efficient feature selection and machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Identification of Inflammation-Related Diagnostic Biomarker and Molecular Subtypes in Ulcerative Colitis Based on Machine Learning.Digestive diseases and sciences · 2026Article
- Diagnostic potential of neutrophil extracellular traps in ulcerative colitis: a gene-based predictive model.Molecular and cellular biochemistry · 2026Article
- Hybrid deep learning framework based on EfficientViT for classification of gastrointestinal diseases.Scientific reports · 2025Article
- FastColitisDetector-XAI: An efficient AI model utilizing sparse Autoencoder with explainable AI for ulcerative colitis diagnosis.MethodsX · 2025Article
- Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images.Current medical imaging · 2025Article
- Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.Frontiers in immunology · 2025Review
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
- Inflammatory bowel disease genomics, transcriptomics, proteomics and metagenomics meet artificial intelligence.United European gastroenterology journal · 2024Review
- A hybrid approach of vision transformers and CNNs for detection of ulcerative colitis.Scientific reports · 2024Article
- Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection.Diagnostics (Basel, Switzerland) · 2024Article
- The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial intelligence and machine learning technologies in ulcerative colitis.Therapeutic advances in gastroenterology · 2024Review
- The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease-The Future Is Now.Diagnostics (Basel, Switzerland) · 2023Review
- Development of a 32-gene signature using machine learning for accurate prediction of inflammatory bowel disease.Cell regeneration (London, England) · 2023Article
- Analysis of cuproptosis-related genes in Ulcerative colitis and immunological characterization based on machine learning.Frontiers in medicine · 2023Article
- Effect and Mechanism of Pharmaceutical Excipients on Berberine to Alleviate Ulcerative Colitis via Regulating Gut Microbiota.Molecules (Basel, Switzerland) · 2022Article
- Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods.Scientific reports · 2022Article
- Machine Learning Predictive Outcomes Modeling in Inflammatory Bowel Diseases.Inflammatory bowel diseases · 2022Review
- Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease - What the Clinician Needs to Know.Journal of Crohn's & colitis · 2022Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Ulcerative colitis (UC) is one of the most common forms of inflammatory bowel disease (IBD) characterized by inflammation of the mucosal layer of the colon. Diagnosis of UC is based on clinical symptoms, and then confirmed based on endoscopic, histologic and laboratory findings. Feature selection and machine learning have been previously used for creating models to facilitate the diagnosis of certain diseases. In this work, we used a recently developed feature selection algorithm (DRPT) combined with a support vector machine (SVM) classifier to generate a model to discriminate between healthy subjects and subjects with UC based on the expression values of 32 genes in colon samples. We validated our model with an independent gene expression dataset of colonic samples from subjects in active and inactive periods of UC. Our model perfectly detected all active cases and had an average precision of 0.62 in the inactive cases. Compared with results reported in previous studies and a model generated by a recently published software for biomarker discovery using machine learning (BioDiscML), our final model for detecting UC shows better performance in terms of average precision.
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