Ensemble learning of genetic networks from time-series expression data

Cited 14 time in scopus
Metadata Downloads
Ensemble learning of genetic networks from time-series expression data
D Nam; Sung Ho Yoon; Jihyun Kim
Bibliographic Citation
Bioinformatics, vol. 23, no. 23, pp. 3225-3231
Publication Year
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.
Oxford Univ Press
Appears in Collections:
1. Journal Articles > Journal Articles
Files in This Item:
  • There are no files associated with this item.

Items in OpenAccess@KRIBB are protected by copyright, with all rights reserved, unless otherwise indicated.