An ensemble method for gene discovery based on DNA microarray data.
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 simultaneously. 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 disease-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).
Li
Xia
X
Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China. Lixia@ems.hrbmu.edu.cn
Rao
Shaoqi
S
Zhang
Tianwen
T
Guo
Zheng
Z
Zhang
Qingpu
Q
Moser
Kathy L
KL
Topol
Eric J
EJ
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D019992:Databases as Topic; D023281:Genomics / Q000379:methods*; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software; D013223:Statistics as Topic / Q000379:methods