CrossRef Text and Data Mining
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Application of Random Forests to Association Studies Using Mitochondrial Single Nucleotide Polymorphisms.
Yoonhee Kim, Ho Kim
Genomics Inform. 2007;5(4):168-173.

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The Association of Single Nucleotide Polymorphisms with Cancer Risk
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Marker selection for whole-genome association studies with two-stage designs using dense single-nucleotide polymorphisms
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Two-stage approach for identifying single-nucleotide polymorphisms associated with rheumatoid arthritis using random forests and Bayesian networks
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Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests
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Selection of Single Nucleotide Polymorphisms for Association Studies in Candidate Genes
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Combined use of principal component analysis and random forests identify population-informative single nucleotide polymorphisms: application in cattle breeds
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A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms
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DigiPINS: A database for vertebrate exonic single nucleotide polymorphisms and its application to cancer association studies
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Using cases to strengthen inference on the association between single nucleotide polymorphisms and a secondary phenotype in genome-wide association studies
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Increasing Power of Genome-Wide Association Studies by Collecting Additional Single-Nucleotide Polymorphisms
Genetics. 2011;188(2):449-460   Crossref logo
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