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Revealing differences in gene network inference algorithms on the network level by ensemble methods

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Article numberbtq259
Pages (from-to)1738-1744
Number of pages7
Issue number14
Publication statusPublished - 25 May 2010
Externally publishedYes
Publication typeA1 Journal article-refereed


Motivation: 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.