Reverse Engineering of Gene Regulatory Networks: A Comparative Study. (22nd April 2009)

• Record Type: Journal Article
• Authors: Hache, Hendrik; Lehrach, Hans; Herwig, Ralf
• Other Names: Repsilber Dirk Academic Editor.
• Is Part Of: EURASIP journal on bioinformatics and systems biology. Volume 2009(2009)
• Publication Date: 2009-04-22
• Subjects: Bioinformatics -- Periodicals; Systems biology -- Periodicals; Systems Biology; Signal Processing, Computer-Assisted; Bio-informatique; Biologie systémique; Bioinformatics; Systems biology; Systems Biology; Bioinformatics; Electronic journals; Periodical; Fulltext; Internet Resources; Periodicals; Periodicals; 570.285
• DOI: 10.1155/2009/617281
• Languages: English
• ISSNs: 1687-4145

Abstract

Abstract : Reverse engineering of gene regulatory networks has been an intensively studied topic in bioinformatics since it constitutes an intermediate step from explorative to causative gene expression analysis. Many methods have been proposed through recent years leading to a wide range of mathematical approaches. In practice, different mathematical approaches will generate different resulting network structures, thus, it is very important for users to assess the performance of these algorithms. We have conducted a comparative study with six different reverse engineering methods, including relevance networks, neural networks, and Bayesian networks. Our approach consists of the generation of defined benchmark data, the analysis of these data with the different methods, and the assessment of algorithmic performances by statistical analyses. Performance was judged by network size and noise levels. The results of the comparative study highlight the neural network approach as best performing method among those under study.