Literature DB >> 11793666

Using data mining to address heterogeneity in the Southampton data.

C J Chang1, C S Fann.   

Abstract

When analyzing complex traits such as asthma, heterogeneity needs to be assumed. With this in mind, to identify a more homogeneous group of asthmatic patients, we analyzed the Southampton data using the data mining technique known as the regression tree method and the two most inheritable quantitative phenotypes (LnIgE and RAST) as the target variables. Two-point and multipoint nonparametric linkage analyses were carried out using one of the subgroups as affected. In addition, we performed quantitative trait loci nonparametric linkage analysis using each phenotype as the outcome. The results from the affected-sibpairs method and quantitative linkage analysis were compared.

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Year:  2001        PMID: 11793666     DOI: 10.1002/gepi.2001.21.s1.s180

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  2 in total

1.  A genome-wide scan using tree-based association analysis for candidate loci related to fasting plasma glucose levels.

Authors:  Chien-Hsiun Chen; Chee Jen Chang; Wei-Shiung Yang; Chun-Liang Chen; Cathy S J Fann
Journal:  BMC Genet       Date:  2003-12-31       Impact factor: 2.797

2.  Screening large-scale association study data: exploiting interactions using random forests.

Authors:  Kathryn L Lunetta; L Brooke Hayward; Jonathan Segal; Paul Van Eerdewegh
Journal:  BMC Genet       Date:  2004-12-10       Impact factor: 2.797

  2 in total

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