tailieunhanh - Báo cáo hóa học: " Research Article Reverse Engineering of Gene Regulatory Networks: A Comparative Study"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Reverse Engineering of Gene Regulatory Networks: A Comparative Study | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2009 Article ID 617281 12 pages doi 2009 617281 Research Article Reverse Engineering of Gene Regulatory Networks A Comparative Study Hendrik Hache Hans Lehrach and Ralf Herwig Vertebrate Genomics-Bioinformatics Group Max Planck Institute for Molecular Genetics Ihnestrafe 63-73 14195 Berlin Germany Correspondence should be addressed to Hendrik Hache hache@ Received 3 July 2008 Revised 5 December 2008 Accepted 11 March 2009 Recommended by Dirk Repsilber 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. Copyright 2009 Hendrik Hache et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. Introduction Deciphering the complex structure of transcriptional regulation of gene expression by means of computational methods is a challenging task emerged in