ArticleGenome medicine2019
Identifying Crohn's disease signal from variome analysis.
Article in Genome medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 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
- 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
- A deep learning model for prediction of autism status using whole-exome sequencing data.PLoS computational biology · 2024Article
- Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection.Diagnostics (Basel, Switzerland) · 2024Article
- CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.Genome biology · 2024Article
- GFPrint™: A machine learning tool for transforming genetic data into clinical insights.PloS one · 2024Article
- Supervised Machine Learning Classifies Inflammatory Bowel Disease Patients by Subtype Using Whole Exome Sequencing Data.Journal of Crohn's & colitis · 2023Article
- Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease.Genome biology · 2023Article
- Impact of artificial intelligence on prognosis, shared decision-making, and precision medicine for patients with inflammatory bowel disease: a perspective and expert opinion.Annals of medicine · 2023Review
- Predicting embryonic aneuploidy rate in IVF patients using whole-exome sequencing.Human genetics · 2022Article
- Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine.Briefings in bioinformatics · 2022Review
- Editorial: Towards genome interpretation: Computational methods to model the genotype-phenotype relationship.Frontiers in bioinformatics · 2022Article
- Deciphering the role of autophagy in the immunopathogenesis of inflammatory bowel disease.Frontiers in pharmacology · 2022Review
- Review
- Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions.World journal of gastroenterology · 2021Review
- An interpretable low-complexity machine learning framework for robust exome-basedNAR genomics and bioinformatics · 2020Article
- Identifying mutation-driven changes in gene functionality that lead to venous thromboembolism.Human mutation · 2019Article
- The variome concept: focus on CNVariome.Molecular cytogenetics · 2019Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 3 countries.
Funding
Abstract
backgroundAfter years of concentrated research efforts, the exact cause of Crohn's disease (CD) remains unknown. Its accurate diagnosis, however, helps in management and preventing the onset of disease. Genome-wide association studies have identified 241 CD loci, but these carry small log odds ratios and are thus diagnostically uninformative.
methodsHere, we describe a machine learning method-AVA,Dx (Analysis of Variation for Association with Disease)-that uses exonic variants from whole exome or genome sequencing data to extract CD signal and predict CD status. Using the person-specific coding variation in genes from a panel of only 111 individuals, we built disease-prediction models informative of previously undiscovered disease genes. By additionally accounting for batch effects, we were able to accurately predict CD status for thousands of previously unseen individuals from other panels.
resultsAVA,Dx highlighted known CD genes including NOD2 and new potential CD genes. AVA,Dx identified 16% (at strict cutoff) of CD patients at 99% precision and 58% of the patients (at default cutoff) with 82% precision in over 3000 individuals from separately sequenced panels.
conclusionsLarger training panels and additional features, including other types of genetic variants and environmental factors, e.g., human-associated microbiota, may improve model performance. However, the results presented here already position AVA,Dx as both an effective method for revealing pathogenesis pathways and as a CD risk analysis tool, which can improve clinical diagnostic time and accuracy. Links to the AVA,Dx Docker image and the BitBucket source code are at https://bromberglab.org/project/avadx/ .
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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.