國家衛生研究院 NHRI:Item 3990099045/1734
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    题名: Tree-structured supervised learning and the genetics of hypertension
    作者: Huang, J;Lin, A;Narasimhan, B;Quertermous, T;Hsiung, CA;Ho, LT;Grove, JS;Olivier, M;Ranade, K;Risch, NJ;Shen, RA
    贡献者: Division of Biostatistics and Bioinformatics
    摘要: This paper is about an algorithm, FlexTree, for general supervised learning. It extends the binary tree-structured approach (Classification and Regression Trees, CART) although it differs greatly in its selection and combination of predictors. It is particularly applicable to assessing interactions: gene by gene and gene by environment as they bear on complex disease. One model for predisposition to complex disease involves many genes. Of them, most are pure noise; each of the values that is not the prevalent genotype for the minority of genes that contribute to the signal carries a "score." Scores add. Individuals with scores above an unknown threshold are predisposed to the disease. For the additive score problem and simulated data, FlexTree has cross-validated risk better than many cutting-edge technologies to which it was compared when small fractions of candidate genes carry the signal. For the model where only a precise list of aberrant genotypes is predisposing, there is not a systematic pattern of absolute superiority; however, overall, FlexTree seems better than the other technologies. We tried the algorithm on data from 563 Chinese women, 206 hypotensive, 357 hypertensive, with information on ethnicity, menopausal status, insulin-resistant status, and 21 loci. FlexTree and Logic Regression appear better than the others in terms of Bayes risk. However, the differences are not significant in the usual statistical sense.
    关键词: Multidisciplinary Sciences
    日期: 2004-07-20
    關聯: Proceedings of the National Academy of Sciences of the United States of America. 2004 Jul;101(29):10529-10534.
    Link to: http://dx.doi.org/10.1073/pnas.0403794101
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0027-8424&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000222842700009
    Cited Times(Scopus): http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=3242677689
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