Ensemble learning of genetic networks from time-series expression data.

Citation data:

Bioinformatics (Oxford, England), ISSN: 1367-4811, Vol: 23, Issue: 23, Page: 3225-31

Publication Year:
2007
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Citations 13
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Repository URL:
http://scholarworks.unist.ac.kr/handle/201301/7180
PMID:
17977884
DOI:
10.1093/bioinformatics/btm514
Author(s):
Nam, Dougu; Yoon, Sung Ho; Kim, Jihyun F.
Publisher(s):
Oxford University Press (OUP); OXFORD UNIV PRESS
Tags:
Biochemistry, Genetics and Molecular Biology; Computer Science; SACCHAROMYCES-CEREVISIAE; REGULATORY NETWORKS; COMPOUND-MODE; CYCLE
article description
Inferring genetic networks from time-series expression data has been a great deal of interest. In most cases, however, the number of genes exceeds that of data points which, in principle, makes it impossible to recover the underlying networks. To address the dimensionality problem, we apply the subset selection method to a linear system of difference equations. Previous approaches assign the single most likely combination of regulators to each target gene, which often causes over-fitting of the small number of data.