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Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls
We propose an effective machine learning approach to identify group of interacting single nucleotide polymorphisms (SNPs), which contribute most to the breast cancer (BC) risk by assuming dependencies among BCAC iCOGS SNPs. We adopt a gradient tree boosting method followed by an adaptive iterative S...
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| 出版年: | Sci Rep |
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| 主要な著者: | , , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Nature Publishing Group UK
2018
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6120908/ https://ncbi.nlm.nih.gov/pubmed/30177847 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-018-31573-5 |
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