title: Ensemble learning of genetic networks from time-series expression data.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  The MATLAB codes are available upon request from the authors.
authors.affiliation: Korea Research Institute of Bioscience and Biotechnology (KRIBB), PO Box 115, Yuseong, Daejeon 305-600, Republic of Korea. dunam@nims.re.kr
authors.forename: Dougu
authors.initials: D
authors.lastname: Nam
authors.forename: Sung Ho
authors.initials: SH
authors.lastname: Yoon
authors.forename: Jihyun F
authors.initials: JF
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D013997:Time Factors
chemical_list: D020543:Proteome