%0 Journal Article
%T An ensemble method for gene discovery based on DNA microarray data
%A LI Xia
%A RAO Shaoqi
%A ZHANG Tianwen GUO Zheng
%A ZHANG Qingpu Kathy L MOSER Eric J TOPOL
%A <br>Kathy L. MOSER
%A Eric J. TOPOL
%J 中国科学C辑(英文版)
%D 2004
%I Springer
%X The advent of DNA microarray technology has offered the promise of casting new insights onto deciphering secrets of life by monitoring activities of thousands of genes simulta-neously. Current analyses of microarray data focus on precise classification of biological types, for example, tumor versus normal tissues. A further scientific challenging task is to extract dis-ease-relevant genes from the bewildering amounts of raw data, which is one of the most critical themes in the post-genomic era, but it is generally ignored due to lack of an efficient approach. In this paper, we present a novel ensemble method for gene extraction that can be tailored to fulfill multiple biological tasks including (i) precise classification of biological types; (ii) disease gene mining; and (iii) target-driven gene networking. We also give a numerical application for (i) and (ii) using a public microarrary data set and set aside a separate paper to address (iii).
%K microarrays
%K ensemble decision
%K recursive partition tree
%K feature gene selection<br>
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=180CF3A72E750F3261A8A60EDC957784&aid=792BE73E3D8268113A41C6926BC8D508&yid=D0E58B75BFD8E51C&vid=F4B561950EE1D31A&iid=94C357A881DFC066&sid=7D6CD8918B045FD4&eid=EB58C3052341AAA3&journal_id=1674-7305&journal_name=ScienceChina.Lifesciences&referenced_num=9&reference_num=25