ArticleScientific reports2018
Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed.
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- Identifying Significant SNPs of the Total Number of Piglets Born and Their Relationship with Leg Bumps in Pigs.Biology · 2024Article
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- Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York.International journal of medical informatics · 2024Article
- Artificial intelligence for dementia genetics and omics.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.Sensors (Basel, Switzerland) · 2023Article
- Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference.Network neuroscience (Cambridge, Mass.) · 2023Article
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- Genome-wide association study as a powerful tool for dissecting competitive traits in legumes.Frontiers in plant science · 2023Review
- Multi-feature computational framework for combined signatures of dementia in underrepresented settings.Journal of neural engineering · 2022Article
- Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations.Communications biology · 2022Article
- Risk score prediction model based on single nucleotide polymorphism for predicting malaria: a machine learning approach.BMC bioinformatics · 2022Article
- Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women.BMC cancer · 2022Article
- Artificial intelligence in clinical research of cancers.Briefings in bioinformatics · 2022Article
- Article
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7 authors.
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Abstract
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 high BC risk-predictive potential. We also propose a support vector machine formed by the identified SNPs to classify BC cases and controls. Our approach achieves mean average precision (mAP) of 72.66, 67.24 and 69.25 in discriminating BC cases and controls in KBCP, OBCS and merged KBCP-OBCS sample sets, respectively. These results are better than the mAP of 70.08, 63.61 and 66.41 obtained by using a polygenic risk score model derived from 51 known BC-associated SNPs, respectively, in KBCP, OBCS and merged KBCP-OBCS sample sets. BC subtype analysis further reveals that the 200 identified KBCP SNPs from the proposed method performs favorably in classifying estrogen receptor positive (ER+) and negative (ER-) BC cases both in KBCP and OBCS data. Further, a biological analysis of the identified SNPs reveals genes related to important BC-related mechanisms, estrogen metabolism and apoptosis.
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