ArticleHealthcare (Basel, Switzerland)2022
Artificial Intelligence Analysis of Celiac Disease Using an Autoimmune Discovery Transcriptomic Panel Highlighted Pathogenic Genes including BTLA.
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 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 24 citations in OpenAlex.
- From photosensitivity to autoimmunity: the role of AI metabolomics in PCT-celiac disease overlap.Annals of medicine and surgery (2012) · 2026Article
- Artificial Intelligence in Gastrointestinal Endoscopy: Current Advances, Clinical Integration, and Future Directions: A Narrative Review.Clinical and experimental gastroenterology · 2026Review
- Intraepithelial Lymphocytes and LAIR1 Expression in Celiac Disease.Biomedicines · 2025Article
- Article
- Toward Precision Medicine in Celiac Disease: Emerging Roles of Multiomics and Artificial Intelligence.Analytical cellular pathology (Amsterdam) · 2025Review
- Prediction of COVID-19 Hospitalization and Mortality Using Artificial Intelligence.Healthcare (Basel, Switzerland) · 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
- Is Celiac Disease (CD) Prevalent in Patients with Multiple Sclerosis (MS): A Systematic Review and Meta-Analysis.Multiple sclerosis international · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Celiac disease is a common immune-related inflammatory disease of the small intestine caused by gluten in genetically predisposed individuals. This research is a proof-of-concept exercise focused on using Artificial Intelligence (AI) and an autoimmune discovery gene panel to predict and model celiac disease. Conventional bioinformatics, gene set enrichment analysis (GSEA), and several machine learning and neural network techniques were used on a publicly available dataset (GSE164883). Machine learning and deep learning included 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 (multilayer perceptron). As a result, the gene panel predicted celiac disease with high accuracy (95-100%). Several pathogenic genes were identified, some of the immune checkpoint and immuno-oncology pathways. They included
Indexed as
Identifiers
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.