Logistic regression trees for initial selection of interesting loci in case-control studies

被引：0
作者：
Radoslav Z Nickolov
Valentin B Milanov
机构：
[1] Fayetteville State University,Department of Mathematics and Computer Science
来源：
BMC Proceedings | / 1卷 / Suppl 1期
关键词：
Logistic Tree; Multifactor Dimensionality Reduction; Genetic Analysis Workshop; Causative Locus; Interesting Marker;
D O I：
10.1186/1753-6561-1-S1-S57
中图分类号：
学科分类号：
摘要：
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.
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