ArticleScientific reports2023
Gene-specific machine learning for pathogenicity prediction of rare BRCA1 and BRCA2 missense variants.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 10 citations in OpenAlex.
- HBOCpred: An Ensemble Framework for Prioritisation of Germline Missense Variants in Hereditary Breast and Ovarian Cancer.International journal of molecular sciences · 2026Article
- Determining a role for Patient and Public Involvement and Engagement (PPIE) in genomic data governance for cancer care.European journal of human genetics : EJHG · 2026Article
- Disease- and gene-specific deep learning for pathogenicity prediction of rare missense variants in cancer predisposition genes.BioData mining · 2026Article
- AI-based methods for the assessment of DNA damage and repair mechanisms.Frontiers in systems biology · 2026Review
- A machine learning framework for predictive interpretation of variants of uncertain significance in hereditary cancer.Frontiers in systems biology · 2026Article
- Clinically interpretable deep learning for breast cancer missense variant pathogenicity prediction.Frontiers in bioinformatics · 2026Article
- Artificial intelligence in cancer genomics: refining diagnosis of hereditary breast and ovarian cancer syndromes.Annals of medicine and surgery (2012) · 2026Article
- Precision in prediction: tailoring machine learning models for breast cancer missense variants pathogenicity prediction.Briefings in bioinformatics · 2025Article
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- Combining Molecular Dynamics and Machine Learning to Predict Drug Resistance Causing Variants of BRAF in Colorectal Cancer.Molecules (Basel, Switzerland) · 2025Article
- Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities.Human genomics · 2025Review
- Exploring the promoter regions of cancer predisposition genes in patients with triple-negative breast cancer reveals the presence of rare germline variants.The oncologist · 2025Article
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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
Machine learning-based pathogenicity prediction helps interpret rare missense variants of BRCA1 and BRCA2, which are associated with hereditary cancers. Recent studies have shown that classifiers trained using variants of a specific gene or a set of genes related to a particular disease perform better than those trained using all variants, due to their higher specificity, despite the smaller training dataset size. In this study, we further investigated the advantages of "gene-specific" machine learning compared to "disease-specific" machine learning. We used 1068 rare (gnomAD minor allele frequency (MAF) < 0.005) missense variants of 28 genes associated with hereditary cancers for our investigation. Popular machine learning classifiers were employed: regularized logistic regression, extreme gradient boosting, random forests, support vector machines, and deep neural networks. As features, we used MAFs from multiple populations, functional prediction and conservation scores, and positions of variants. The disease-specific training dataset included the gene-specific training dataset and was > 7 × larger. However, we observed that gene-specific training variants were sufficient to produce the optimal pathogenicity predictor if a suitable machine learning classifier was employed. Therefore, we recommend gene-specific over disease-specific machine learning as an efficient and effective method for predicting the pathogenicity of rare BRCA1 and BRCA2 missense variants.
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