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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...

Täydet tiedot

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Bibliografiset tiedot
Julkaisussa:Sci Rep
Päätekijät: Behravan, Hamid, Hartikainen, Jaana M., Tengström, Maria, Pylkäs, Katri, Winqvist, Robert, Kosma, Veli–Matti, Mannermaa, Arto
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: Nature Publishing Group UK 2018
Aiheet:
Linkit: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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