ArticleBioinformatics (Oxford, England)2012
Interaction-based feature selection and classification for high-dimensional biological data.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Test-fairness deep learning with influence score.PLOS digital health · 2026Article
- The molecular pathogenesis of placenta accreta spectrum disorder and its clinical applications.Archives of gynecology and obstetrics · 2025Review
- A generalized covariate-adjusted top-scoring pair algorithm with applications to diabetic kidney disease stage classification in the Chronic Renal Insufficiency Cohort (CRIC) Study.BMC bioinformatics · 2023Article
- Biomarker interaction selection and disease detection based on multivariate gain ratio.BMC bioinformatics · 2022Article
- Systems biology and machine learning approaches identify drug targets in diabetic nephropathy.Scientific reports · 2021Article
- Approaches to treatment effect heterogeneity in the presence of confounding.Statistics in medicine · 2019Article
- Stratified polygenic risk prediction model with application to CAGI bipolar disorder sequencing data.Human mutation · 2017Article
- Framework for making better predictions by directly estimating variables' predictivity.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- A fast and powerful W-test for pairwise epistasis testing.Nucleic acids research · 2016Article
- Informative gene selection and the direct classification of tumors based on relative simplicity.BMC bioinformatics · 2016Article
- Why significant variables aren't automatically good predictors.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Article
- Informative gene selection and direct classification of tumor based on Chi-square test of pairwise gene interactions.BioMed research international · 2014Article
- Considering interactive effects in the identification of influential regions with extremely rare variants via fixed bin approach.BMC proceedings · 2014Article
- Discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics.BMC proceedings · 2014Article
- A robust model-free approach for rare variants association studies incorporating gene-gene and gene-environmental interactions.PloS one · 2013Article
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4 authors.
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Abstract
motivationEpistasis or gene-gene interaction has gained increasing attention in studies of complex diseases. Its presence as an ubiquitous component of genetic architecture of common human diseases has been contemplated. However, the detection of gene-gene interaction is difficult due to combinatorial explosion.
resultsWe present a novel feature selection method incorporating variable interaction. Three gene expression datasets are analyzed to illustrate our method, although it can also be applied to other types of high-dimensional data. The quality of variables selected is evaluated in two ways: first by classification error rates, then by functional relevance assessed using biological knowledge. We show that the classification error rates can be significantly reduced by considering interactions. Secondly, a sizable portion of genes identified by our method for breast cancer metastasis overlaps with those reported in gene-to-system breast cancer (G2SBC) database as disease associated and some of them have interesting biological implication. In summary, interaction-based methods may lead to substantial gain in biological insights as well as more accurate prediction.
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