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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Detalles Bibliográficos
Autores principales: Behravan, Hamid, Hartikainen, Jaana M., Tengström, Maria, Pylkäs, Katri, Winqvist, Robert, Kosma, Veli–Matti, Mannermaa, Arto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6120908/
https://www.ncbi.nlm.nih.gov/pubmed/30177847
http://dx.doi.org/10.1038/s41598-018-31573-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6120908/
https://www.ncbi.nlm.nih.gov/pubmed/30177847
http://dx.doi.org/10.1038/s41598-018-31573-5

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