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Nam, Dougu
Statistical Genomics Lab
Research Interests
  • Gene network, pathway analysis, biclustering, disease classification

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Ensemble learning of genetic networks from time-series expression data

Cited 13 times inthomson ciCited 12 times inthomson ci
Title
Ensemble learning of genetic networks from time-series expression data
Author
Nam, DouguYoon, Sung HoKim, Jihyun F.
Keywords
SACCHAROMYCES-CEREVISIAE; REGULATORY NETWORKS; COMPOUND-MODE; CYCLE
Issue Date
200712
Publisher
OXFORD UNIV PRESS
Citation
BIOINFORMATICS, v.23, no.23, pp.3225 - 3231
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.
URI
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DOI
http://dx.doi.org/10.1093/bioinformatics/btm514
ISSN
1367-4803
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