ArticleAmerican journal of human genetics2013
Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease.
Article in American journal of human genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 4 of them syntheses that pooled it.
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.
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Who cites it
105 citing papers in PubMed, 4 syntheses or guidelines pooled it, 198 citations in OpenAlex.
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Genome-wide meta-analysis and replication studies in multiple ethnicities identify novel adolescent idiopathic scoliosis susceptibility loci.Human molecular genetics · 2018Pooled it
- Common polygenic variation in coeliac disease and confirmation of ZNF335 and NIFA as disease susceptibility loci.European journal of human genetics : EJHG · 2016Pooled it
- Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease.Nature genetics · 2014Pooled it
- Leveraging Machine Learning to Predict Warfarin Sensitivity in the Puerto Rican Population: A Pharmacogenomic Approach.International journal of environmental research and public health · 2026Article
- Cystic fibrosis risk variants confer protection against inflammatory bowel disease.Cell genomics · 2026Article
- Multiomics Integration Reveals Microbial Gene Interactions Shaping Host Responses in a DSS-Induced Colitis Mouse Model.Journal of microbiology and biotechnology · 2025Article
- Investigating the role of the Pon1-rs854560 (L55M) SNP in colorectal Cancer susceptibility.Journal of cancer research and clinical oncology · 2025Article
- Article
- Eleven Grand Challenges for Inflammatory Bowel Disease Genetics and Genomics.Inflammatory bowel diseases · 2025Review
- Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades.Frontiers in immunology · 2025Review
- Machine learning in the differential diagnosis of ulcerative colitis and Crohn's disease: a systematic review.Translational gastroenterology and hepatology · 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
- Machine learning-based prediction of DNA G-quadruplex folding topology with G4ShapePredictor.Scientific reports · 2024Article
- Optimizing and benchmarking polygenic risk scores with GWAS summary statistics.Genome biology · 2024Article
- Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection.Diagnostics (Basel, Switzerland) · 2024Article
- Random forest identifies predictors of discharge destination following total shoulder arthroplasty.JSES international · 2024Article
- Pathogenesis and precision medicine for predicting response in inflammatory bowel disease: advances and future directions.eGastroenterology · 2024Review
- Review
45 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 3 countries.
Funding
Abstract
We performed risk assessment for Crohn's disease (CD) and ulcerative colitis (UC), the two common forms of inflammatory bowel disease (IBD), by using data from the International IBD Genetics Consortium's Immunochip project. This data set contains ~17,000 CD cases, ~13,000 UC cases, and ~22,000 controls from 15 European countries typed on the Immunochip. This custom chip provides a more comprehensive catalog of the most promising candidate variants by picking up the remaining common variants and certain rare variants that were missed in the first generation of GWAS. Given this unprecedented large sample size and wide variant spectrum, we employed the most recent machine-learning techniques to build optimal predictive models. Our final predictive models achieved areas under the curve (AUCs) of 0.86 and 0.83 for CD and UC, respectively, in an independent evaluation. To our knowledge, this is the best prediction performance ever reported for CD and UC to date.
Indexed as
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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.