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


Ensemble learning of genetic networks from time-series expression data

DC Field Value Language Nam, Dougu ko Yoon, Sung Ho ko Kim, Jihyun F. ko 2014-10-16T01:41:59Z - 2014-10-13 ko 2007-12 ko
dc.identifier.citation BIOINFORMATICS, v.23, no.23, pp.3225 - 3231 ko
dc.identifier.issn 1367-4803 ko
dc.identifier.uri -
dc.description.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. ko
dc.description.statementofresponsibility close -
dc.language 영어 ko
dc.publisher OXFORD UNIV PRESS ko
dc.title Ensemble learning of genetic networks from time-series expression data ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-36549040919 ko
dc.identifier.wosid 000251334800016 ko
dc.type.rims ART ko
dc.description.wostc 13 *
dc.description.scopustc 12 * 2015-05-06 * 2014-10-13 *
dc.identifier.doi 10.1093/bioinformatics/btm514 ko
dc.identifier.url ko
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