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Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls.

Scientific reports - 3 Sept 2018

Behravan Hamid, Hartikainen Jaana M, Tengström Maria, Pylkäs Katri, Winqvist Robert, Kosma Veli-Matti, Mannermaa Arto

Abstract excerpt

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 SNP search to capture complex non-linear SNP-SNP interactions and consequently, obtain group of interacting SNPs with...

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PMID: 30177847
DOI: 10.1038/s41598-018-31573-5
PMCID: PMC6120908

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