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- W2079342551 abstract "Modern genetic epidemiology faces the challenge of dealing with hundreds of thousands of genetic markers. The selection of a small initial subset of interesting markers for further investigation can greatly facilitate genetic studies. In this contribution we suggest the use of a logistic regression tree algorithm known as logistic tree with unbiased selection. Using the simulated data provided for Genetic Analysis Workshop 15, we show how this algorithm, with incorporation of multifactor dimensionality reduction method, can reduce an initial large pool of markers to a small set that includes the interesting markers with high probability." @default.
- W2079342551 created "2016-06-24" @default.
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- W2079342551 date "2007-12-01" @default.
- W2079342551 modified "2023-10-14" @default.
- W2079342551 title "Logistic regression trees for initial selection of interesting loci in case-control studies" @default.
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- W2079342551 doi "https://doi.org/10.1186/1753-6561-1-s1-s57" @default.
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