Loading...
|
Please use this identifier to cite or link to this item:
http://ir.nhri.org.tw/handle/3990099045/1734
|
| Title: | Tree-structured supervised learning and the genetics of hypertension |
| Authors: | Huang, J;Lin, A;Narasimhan, B;Quertermous, T;Hsiung, CA;Ho, LT;Grove, JS;Olivier, M;Ranade, K;Risch, NJ;Shen, RA |
| Contributors: | Division of Biostatistics and Bioinformatics |
| Abstract: | 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. |
| Keywords: | Multidisciplinary Sciences |
| Date: | 2004-07-20 |
| Relation: | 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 |
| Appears in Collections: | [熊昭] 期刊論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 000222842700009.pdf | | 288Kb | Adobe PDF | 361 | View/Open |
|
All items in NHRI are protected by copyright, with all rights reserved.
|
