An ensemble method for gene discovery based on DNA microarray data  

作　　者：Kathy L. MOSER Eric J. TOPOL 

机构地区：[1]Department of Medicine, Institute of Human Genetics University of Minnesota, Minnesota 55455, USA ,Center for Cardiovascular Genetics, Department of Cardiovascular Medicine, the Cleveland Clinic Foundation 9500 Euclid Avenue, Cleveland, Ohio 44195, USADepartment of Molecular Cardiology, the Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA

出　　处：《Science China(Life Sciences)》2004年第5期396-405,共10页中国科学（生命科学英文版）

基　　金：This work was supported in part by the National Natural Science Foundation of China(Grant Nos.30170515 and 30370798);the Chinese 863 Program(Grant No.2003AA2Z2051 and 2002AA2Z2052);the 211 Project;the Tenth'Five-year'Plan;Harbin Medical University.

摘　　要：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).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).

关 键 词：microarrays ensemble decision recursive PARTITION tree feature gene selection. 

分 类 号：Q78[生物学—分子生物学]