ArticleHealthcare (Basel, Switzerland)2022
Artificial Intelligence Analysis of Ulcerative Colitis Using an Autoimmune Discovery Transcriptomic Panel.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 14 citations in OpenAlex.
- Integrative Analysis of Intestinal Transcriptomes UnderscoresBioinformatics and biology insights · 2026Article
- Intraepithelial Lymphocytes and LAIR1 Expression in Celiac Disease.Biomedicines · 2025Article
- Suppressor of cytokine signaling 2 modulates regulatory T cell activity to suppress liver hepatocellular carcinoma growth and metastasis.World journal of gastroenterology · 2025Article
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
- A hybrid approach of vision transformers and CNNs for detection of ulcerative colitis.Scientific reports · 2024Article
- Celiac Disease Deep Learning Image Classification Using Convolutional Neural Networks.Journal of imaging · 2024Article
- Artificial intelligence and classification of mature lymphoid neoplasms.Exploration of targeted anti-tumor therapy · 2024Review
- The pathobiology of follicular lymphoma.Journal of clinical and experimental hematopathology : JCEH · 2023Article
- Article
- Artificial Intelligence Analysis of Celiac Disease Using an Autoimmune Discovery Transcriptomic Panel Highlighted Pathogenic Genes including BTLA.Healthcare (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Ulcerative colitis is a bowel disease of unknown cause. This research is a proof-of-concept exercise focused on determining whether it is possible to identify the genes associated with ulcerative colitis using artificial intelligence. Several machine learning and artificial neural networks analyze using an autoimmune discovery transcriptomic panel of 755 genes to predict and model ulcerative colitis versus healthy donors. The dataset GSE38713 of 43 cases from the Hospital Clinic of Barcelona was selected, and 16 models were used, including C5, logistic regression, Bayesian network, discriminant analysis, KNN algorithm, LSVM, random trees, SVM, Tree-AS, XGBoost linear, XGBoost tree, CHAID, Quest, C&R tree, random forest, and neural network. Conventional analysis, including volcano plot and gene set enrichment analysis (GSEA), were also performed. As a result, ulcerative colitis was successfully predicted with several machine learning techniques and artificial neural networks (multilayer perceptron), with an overall accuracy of 95-100%, and relevant pathogenic genes were highlighted. One of them, programmed cell death 1 ligand 1 (
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.