Data mining of the GAW14 simulated data using rough set theory and tree-based methods

中國醫藥大學機構典藏 China Medical University Repository, Taiwan > 中醫學院 > 中醫學系暨碩博班 > 會議論文 > Item 310903500/27103

請使用永久網址來引用或連結此文件: http://ir.cmu.edu.tw/ir/handle/310903500/27103

題名:	Data mining of the GAW14 simulated data using rough set theory and tree-based methods
作者:	Wei, LY;Huang, CL;Chen, CH
貢獻者:	中醫學院中醫系 Huafan Univ, Acad Sinica, Inst Biomed Sci, Taipei, Taiwan;Huafan Univ, Dept Informat Management, Taipei, Taiwan;Natl Kaohsiung First Univ Sci & Technol, Dept Informat Management, Kaohsiung, Taiwan;China Med Univ, Coll Chinese Med, Taichung, Taiwan
日期:	2005
上傳時間:	2010-09-20 13:43:38 (UTC+8)
出版者:	BIOMED CENTRAL LTD
摘要:	Rough set theory and decision trees are data mining methods used for dealing with vagueness and uncertainty. They have been utilized to unearth hidden patterns in complicated datasets collected for industrial processes. The Genetic Analysis Workshop 14 simulated data were generated using a system that implemented multiple correlations among four consequential layers of genetic data (disease-related loci, endophenotypes, phenotypes, and one disease trait). When information of one layer was blocked and uncertainty was created in the correlations among these layers, the correlation between the first and last layers (susceptibility genes and the disease trait in this case), was not easily directly detected. In this study, we proposed a two-stage process that applied rough set theory and decision trees to identify genes susceptible to the disease trait. During the first stage, based on phenotypes of subjects and their parents, decision trees were built to predict trait values. Phenotypes retained in the decision trees were then advanced to the second stage, where rough set theory was applied to discover the minimal subsets of genes associated with the disease trait. For comparison, decision trees were also constructed to map susceptible genes during the second stage. Our results showed that the decision trees of the first stage had accuracy rates of about 99% in predicting the disease trait. The decision trees and rough set theory failed to identify the true disease-related loci.
關聯:	BMC GENETICS 6(suppl.1):133
顯示於類別:	[中醫學系暨碩博班] 會議論文

文件中的檔案:

沒有與此文件相關的檔案.

在CMUR中所有的資料項目都受到原著作權保護.

TAIR相關文章

資料載入中.....