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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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| Publicado en: | Sci Rep |
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| Autores principales: | , , , , , , |
| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
Nature Publishing Group UK
2018
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| Materias: | |
| Acceso en línea: | 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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