ArticleScientific reports2020
Predicting breast cancer risk using interacting genetic and demographic factors and machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of prediction models for risk of breast cancer.BMC cancer · 2025Pooled it
- Breast cancer risk prediction using machine learning: a systematic review.Frontiers in oncology · 2024Pooled it
- WDCN: a comprehensive neural network based approach for estimating breast cancer risk.Briefings in bioinformatics · 2026Article
- Long Intergenic Non-Coding RNA 00511 (LINC00511) Genetic Variations and Haplotypes in Breast Cancer: A Case-Controlled Study and Bioinformatics Analysis.International journal of molecular sciences · 2025Article
- Predictive Genetic Testing in the Cancer Management and Prevention.Journal of cancer science and clinical therapeutics · 2025Article
- Deep Learning-Based Breast Tumor Classification Using Shear-Wave Sonoelastography Image Features and Clinical Variables.Advanced biomedical research · 2025Article
- Identification of a Novel Biomarker Panel for Breast Cancer Screening.International journal of molecular sciences · 2024Article
- Article
- Comprehensive breast cancer risk analysis with whole exome sequencing and the prevalence ofBiomedical reports · 2024Article
- Critical Risk Assessment, Diagnosis, and Survival Analysis of Breast Cancer.Diagnostics (Basel, Switzerland) · 2024Article
- Identifying Predictors of Neck Disability in Patients with Cervical Pain Using Machine Learning Algorithms: A Cross-Sectional Correlational Study.Journal of clinical medicine · 2024Article
- Breast Cancer Risk Factors among Women with Solid Breast Lesions.Clinics and practice · 2024Article
- A contemporary review of breast cancer risk factors and the role of artificial intelligence.Frontiers in oncology · 2024Review
- Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman.Journal of general internal medicine · 2023Article
- Molecular Characterization and Landscape of Breast cancer Models from a multi-omics Perspective.Journal of mammary gland biology and neoplasia · 2023Review
- Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.Sensors (Basel, Switzerland) · 2023Article
- Overexpression of Estrogen Receptor α in Mammary Glands of Aging Mice Is Associated with a Proliferative Risk Signature and Generation of Estrogen Receptor α-Positive Mammary Adenocarcinomas.The American journal of pathology · 2023Article
- Comparing feature selection and machine learning approaches for predictingFrontiers in neuroinformatics · 2023Article
- Article
- Esr1 but Not CYP19A1 Overexpression in Mammary Epithelial Cells during Reproductive Senescence Induces Pregnancy-Like Proliferative Mammary Disease Responsive to Anti-Hormonals.The American journal of pathology · 2023Article
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5 authors.
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Abstract
Breast cancer (BC) is a multifactorial disease and the most common cancer in women worldwide. We describe a machine learning approach to identify a combination of interacting genetic variants (SNPs) and demographic risk factors for BC, especially factors related to both familial history (Group 1) and oestrogen metabolism (Group 2), for predicting BC risk. This approach identifies the best combinations of interacting genetic and demographic risk factors that yield the highest BC risk prediction accuracy. In tests on the Kuopio Breast Cancer Project (KBCP) dataset, our approach achieves a mean average precision (mAP) of 77.78 in predicting BC risk by using interacting genetic and Group 1 features, which is better than the mAPs of 74.19 and 73.65 achieved using only Group 1 features and interacting SNPs, respectively. Similarly, using interacting genetic and Group 2 features yields a mAP of 78.00, which outperforms the system based on only Group 2 features, which has a mAP of 72.57. Furthermore, the gene interaction maps built from genes associated with SNPs that interact with demographic risk factors indicate important BC-related biological entities, such as angiogenesis, apoptosis and oestrogen-related networks. The results also show that demographic risk factors are individually more important than genetic variants in predicting BC risk.
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