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Bioinformatics Advance Access originally published online on October 31, 2007
Bioinformatics 2007 23(23):3225-3231; doi:10.1093/bioinformatics/btm514
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Ensemble learning of genetic networks from time-series expression data

Dougu Nam 1,2,*, Sung Ho Yoon 1 and Jihyun F. Kim 1,*

1Korea Research Institute of Bioscience and Biotechnology (KRIBB), PO Box 115, Yuseong, Daejeon 305-600 and 2National Institute for Mathematical Sciences (NIMS), Yuseong, Daejeon 305-340, Republic of Korea

*To whom correspondence should be addressed.


  Abstract

Motivation: 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.

Results: Here, we propose a new algorithm, named LEARNe, which merges the predictions from all the combinations of regulators that have a certain level of likelihood. LEARNe provides more accurate and robust predictions than previous methods for the structure of genetic networks under the linear system model. We tested LEARNe for reconstructing the SOS regulatory network of Escherichia coli and the cell cycle regulatory network of yeast from real experimental data, where LEARNe also exhibited better performances than previous methods.

Availability: The MATLAB codes are available upon request from the authors.

Contact: dunam{at}nims.re.kr or jfk{at}kribb.re.kr

Associate Editor: Olga Troyanskaya

Received on July 12, 2007; revised on September 12, 2007; accepted on October 7, 2007

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