ArticleNAR genomics and bioinformatics2021
DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies.
Article in NAR genomics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- Interpreting artificial neural networks to detect genome-wide association signals for complex traits.NAR genomics and bioinformatics · 2026Article
- Transformer-based InsightGWAS improves GERD genetic discovery via pretraining on GWAS for major depressive disorder.Communications biology · 2026Article
- Machine learning-based lineage prediction from antimicrobial susceptibility testing phenotypes forMicrobial genomics · 2026Article
- Predicting VNN resistance in European sea bass using machine learning on high dimensional low sample size data.Frontiers in bioinformatics · 2026Article
- Transformer-based deep learning enhances discovery in migraine GWAS.Nature communications · 2025Article
- The Genetic Data Market: Institutional Governance of Academic/Industry Research Partnerships for the Public Good.The American journal of bioethics : AJOB · 2025Article
- Decoding ancestry-specific genetic risk: interpretable deep feature selection reveals prostate cancer SNP disparities in diverse populations.BioData mining · 2025Article
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- A Talk with ChatGPT: The Role of Artificial Intelligence in Shaping the Future of Cardiology and Electrophysiology.Journal of personalized medicine · 2025Article
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool.AI and ethics · 2025Article
- Leveraging hierarchical structures for genetic block interaction studies using the hierarchical transformer.medRxiv : the preprint server for health sciences · 2025Article
- Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects.Frontiers in pediatrics · 2025Review
- Identifying sepsis susceptibility genes in post-surgical patients using an artificial intelligence approach.Frontiers in medicine · 2025Article
- Applying traditional and machine learning-based GWAS approaches for marker-trait identification in wheat.Frontiers in plant science · 2025Article
- AutoXAI4Omics: an automated explainable AI tool for omics and tabular data.Briefings in bioinformatics · 2024Article
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- Distributed transformer for high order epistasis detection in large-scale datasets.Scientific reports · 2024Article
- Epi-SSA: A novel epistasis detection method based on a multi-objective sparrow search algorithm.PloS one · 2024Article
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Authors and funding
6 authors.
Funding
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Abstract
Deep learning has revolutionized data science in many fields by greatly improving prediction performances in comparison to conventional approaches. Recently, explainable artificial intelligence has emerged as an area of research that goes beyond pure prediction improvement by extracting knowledge from deep learning methodologies through the interpretation of their results. We investigate such explanations to explore the genetic architectures of phenotypes in genome-wide association studies. Instead of testing each position in the genome individually, the novel three-step algorithm, called DeepCOMBI, first trains a neural network for the classification of subjects into their respective phenotypes. Second, it explains the classifiers' decisions by applying layer-wise relevance propagation as one example from the pool of explanation techniques. The resulting importance scores are eventually used to determine a subset of the most relevant locations for multiple hypothesis testing in the third step. The performance of DeepCOMBI in terms of power and precision is investigated on generated datasets and a 2007 study. Verification of the latter is achieved by validating all findings with independent studies published up until 2020. DeepCOMBI is shown to outperform ordinary raw
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