title: Revealing differences in gene network inference algorithms on the network level by ensemble methods.
abstract: BACKGROUND
  The inference of regulatory networks from large-scale expression data holds great promise because of the potentially causal interpretation of these networks. However, due to the difficulty to establish reliable methods based on observational data there is so far only incomplete knowledge about possibilities and limitations of such inference methods in this context.
  
  
  RESULTS
  In this article, we conduct a statistical analysis investigating differences and similarities of four network inference algorithms, ARACNE, CLR, MRNET and RN, with respect to local network-based measures. We employ ensemble methods allowing to assess the inferability down to the level of individual edges. Our analysis reveals the bias of these inference methods with respect to the inference of various network components and, hence, provides guidance in the interpretation of inferred regulatory networks from expression data. Further, as application we predict the total number of regulatory interactions in human B cells and hypothesize about the role of Myc and its targets regarding molecular information processing.
authors.affiliation: Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK.
authors.forename: Gökmen
authors.initials: G
authors.lastname: Altay
authors.forename: Frank
authors.initials: F
authors.lastname: Emmert-Streib
mesh_terms: D000465:Algorithms; D001402:B-Lymphocytes / Q000378:metabolism; D053263:Gene Regulatory Networks; D016259:Genes, myc; D023281:Genomics / Q000379:methods*