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  Unraveling gene regulatory networks from time-resolved gene expression data - a measures comparison study

Hempel, S., Koseska, A., Nikoloski, Z., & Kurths, J. (2011). Unraveling gene regulatory networks from time-resolved gene expression data - a measures comparison study. BMC Bioinformatics, 12, 292. doi:10.1186/1471-2105-12-292.

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Hempel, S.1, Author              
Koseska, A.1, Author
Nikoloski, Z.1, Author              
Kurths, J.2, Author
1Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753310              
2External Organizations, ou_persistent22              


Free keywords: inferring cellular networks mutual information escherichia-coli cluster-analysis series algorithms inference models recognition variables
 Abstract: Background: Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications. Results: Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study. Conclusions: Our study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices.


Language(s): eng - English
 Dates: 2011-07-192011
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISI: ISI:000294361400001
DOI: 10.1186/1471-2105-12-292
ISSN: 1471-2105
URI: ://000294361400001http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3161045/pdf/1471-2105-12-292.pdf?tool=pmcentrez
 Degree: -



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Title: BMC Bioinformatics
Source Genre: Journal
Publ. Info: BioMed Central
Pages: - Volume / Issue: 12 Sequence Number: - Start / End Page: 292 Identifier: ISSN: 1471-2105
CoNE: https://pure.mpg.de/cone/journals/resource/111000136905000