ArticleScientific reports2019
Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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Who cites it
56 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
- Multimodal deep learning enhances genomic risk prediction for cardiometabolic diseases.Briefings in bioinformatics · 2026Article
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- Integrating temporal morphophysiological and genomic markers for precise classification of flowering time in cannabis.Scientific reports · 2026Article
- Improved polygenic risk prediction for alzheimer's disease and related dementias using deep learning: age and APOE-stratified analysis.Alzheimer's research & therapy · 2026Article
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- MINE: maximally informative next experiment-toward a new GWAS experimental design and methodology.G3 (Bethesda, Md.) · 2025Article
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- MINE: a new way to design genetics experiments for discovery.Briefings in bioinformatics · 2025Article
- How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2025Review
- Machine learning in the differential diagnosis of ulcerative colitis and Crohn's disease: a systematic review.Translational gastroenterology and hepatology · 2025Review
- Next-generation non-animal models for inflammatory bowel disease:NAM journal · 2025Review
- Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.Frontiers in immunology · 2025Review
- Inflammatory bowel disease genomics, transcriptomics, proteomics and metagenomics meet artificial intelligence.United European gastroenterology journal · 2024Review
- Computed tomography enterography radiomics and machine learning for identification of Crohn's disease.BMC medical imaging · 2024Article
- The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning.Cancers · 2024Review
- Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection.Diagnostics (Basel, Switzerland) · 2024Article
- Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection.Journal of translational medicine · 2024Article
- Pathogenesis and precision medicine for predicting response in inflammatory bowel disease: advances and future directions.eGastroenterology · 2024Review
- Diagnosis and classification of portosystemic shunts: a machine learning retrospective case-control study.Frontiers in veterinary science · 2024Article
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6 authors.
Funding
Abstract
Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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