ArticleHuman genomics2023
Evaluation of a genetic risk score computed using human chromosomal-scale length variation to predict breast cancer.
Article in Human genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 6 citations in OpenAlex.
- A compact encoding of the genome suitable for machine learning prediction of traits and genetic risk scores.BioData mining · 2025Article
- Improved breast cancer risk prediction using chromosomal-scale length variation.Human genomics · 2025Article
- A contemporary review of breast cancer risk factors and the role of artificial intelligence.Frontiers in oncology · 2024Review
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Authors and funding
2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
introductionThe ability to accurately predict whether a woman will develop breast cancer later in her life, should reduce the number of breast cancer deaths. Different predictive models exist for breast cancer based on family history, BRCA status, and SNP analysis. The best of these models has an accuracy (area under the receiver operating characteristic curve, AUC) of about 0.65. We have developed computational methods to characterize a genome by a small set of numbers that represent the length of segments of the chromosomes, called chromosomal-scale length variation (CSLV).
methodsWe built machine learning models to differentiate between women who had breast cancer and women who did not based on their CSLV characterization. We applied this procedure to two different datasets: the UK Biobank (1534 women with breast cancer and 4391 women who did not) and the Cancer Genome Atlas (TCGA) 874 with breast cancer and 3381 without.
resultsWe found a machine learning model that could predict breast cancer with an AUC of 0.836 95% CI (0.830.0.843) in the UK Biobank data. Using a similar approach with the TCGA data, we obtained a model with an AUC of 0.704 95% CI (0.702, 0.706). Variable importance analysis indicated that no single chromosomal region was responsible for significant fraction of the model results.
conclusionIn this retrospective study, chromosomal-scale length variation could effectively predict whether or not a woman enrolled in the UK Biobank study developed breast cancer.
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