ArticleFundamental research2024
DeepRisk: A deep learning approach for genome-wide assessment of common disease risk.
Article in Fundamental research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- WDCN: a comprehensive neural network based approach for estimating breast cancer risk.Briefings in bioinformatics · 2026Article
- Review
- Data Representation Bias and Conditional Distribution Shift Drive Predictive Performance Disparities in Multi-Population Machine Learning.bioRxiv : the preprint server for biology · 2026Article
- Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias.Seminars in neurology · 2026Review
- PGVDA: a pathway-aggregated genetic dosage framework for interpretable disease classification using machine learning.Briefings in bioinformatics · 2026Article
- Remodeling the tumor dormancy ecosystem to prevent recurrence and metastasis.Signal transduction and targeted therapy · 2026Review
- Benchmarking interpretability of deep learning for predictive genomics: Recall, precision, and variability of feature attribution.PLoS computational biology · 2025Article
- A survey on deep learning for polygenic risk scores.Briefings in bioinformatics · 2025Review
- MetaGeno: a chromosome-wise multi-task genomic framework for ischaemic stroke risk prediction.Briefings in bioinformatics · 2025Article
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- VADEr: Vision Transformer-Inspired Framework for Polygenic Risk Reveals Underlying Genetic Heterogeneity in Prostate Cancer.medRxiv : the preprint server for health sciences · 2025Article
- Deep learning-based polygenic scores enhance generalizability of psychiatric disorders prediction.medRxiv : the preprint server for health sciences · 2025Article
- A review of methods and software for polygenic risk score analysis.PeerJ. Computer science · 2025Article
- Bioinformatics and Biomedical Computing.Fundamental research · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant interest. Although widely applied, traditional polygenic risk scoring methods fall short, as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms (SNPs). This presents a limitation, as genetic diseases often arise from complex interactions between multiple SNPs. To address this challenge, we developed DeepRisk, a biological knowledge-driven deep learning method for modeling these complex, nonlinear associations among SNPs, to provide a more effective method for scoring the risk of common diseases with genome-wide genotype data. Evaluations demonstrated that DeepRisk outperforms existing PRS-based methods in identifying individuals at high risk for four common diseases: Alzheimer's disease, inflammatory bowel disease, type 2 diabetes, and breast cancer.
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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.