ArticleJournal of human genetics2021
Deep neural network improves the estimation of polygenic risk scores for breast cancer.
Article in Journal of human genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed, 72 citations in OpenAlex.
- Integrating DNA methylation biomarkers for breast cancer risk prediction using artificial intelligence.Scientific reports · 2026Article
- Data Representation Bias and Conditional Distribution Shift Drive Predictive Performance Disparities in Multi-Population Machine Learning.bioRxiv : the preprint server for biology · 2026Article
- Clinical Application of Pharmacogenomics in Stroke Management: Current Evidence and Future Directions.Journal of stroke · 2026Review
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- Complex genetic effects linked to plasma protein abundance in the UK Biobank.Nature communications · 2025Article
- Benchmarking interpretability of deep learning for predictive genomics: Recall, precision, and variability of feature attribution.PLoS computational biology · 2025Article
- Advanced machine learning techniques for coronary artery disease risk prediction in early middle-aged adults.BMC medical genomics · 2025Article
- Expanding our understanding of synucleinopathies: proteinopathy, proteinopenia, and lipidopathy.The FEBS journal · 2025Review
- Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities.Human genomics · 2025Review
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- A survey on deep learning for polygenic risk scores.Briefings in bioinformatics · 2025Review
- A compact encoding of the genome suitable for machine learning prediction of traits and genetic risk scores.BioData mining · 2025Article
- Performance of deep-learning-based approaches to improve polygenic scores.Nature communications · 2025Article
- 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
- Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel.BMC genomics · 2025Article
- Efficient blockLASSO for polygenic scores with applications to all of us and UK Biobank.BMC genomics · 2025Article
- Article
- Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement.Genome medicine · 2024Article
- Stacked neural network for predicting polygenic risk score.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polygenic risk scores (PRS) estimate the genetic risk of an individual for a complex disease based on many genetic variants across the whole genome. In this study, we compared a series of computational models for estimation of breast cancer PRS. A deep neural network (DNN) was found to outperform alternative machine learning techniques and established statistical algorithms, including BLUP, BayesA, and LDpred. In the test cohort with 50% prevalence, the Area Under the receiver operating characteristic Curve (AUC) were 67.4% for DNN, 64.2% for BLUP, 64.5% for BayesA, and 62.4% for LDpred. BLUP, BayesA, and LPpred all generated PRS that followed a normal distribution in the case population. However, the PRS generated by DNN in the case population followed a bimodal distribution composed of two normal distributions with distinctly different means. This suggests that DNN was able to separate the case population into a high-genetic-risk case subpopulation with an average PRS significantly higher than the control population and a normal-genetic-risk case subpopulation with an average PRS similar to the control population. This allowed DNN to achieve 18.8% recall at 90% precision in the test cohort with 50% prevalence, which can be extrapolated to 65.4% recall at 20% precision in a general population with 12% prevalence. Interpretation of the DNN model identified salient variants that were assigned insignificant p values by association studies, but were important for DNN prediction. These variants may be associated with the phenotype through nonlinear relationships.
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