Investigating meta-approaches for reconstructing gene networks in a mammalian cellular context.
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Publication Date
2012Journal Title
PLoS One
ISSN
1932-6203
Publisher
Public Library of Science (PLoS)
Volume
7
Issue
1
Pages
e28713
Language
eng
Type
Article
Physical Medium
Print-Electronic
Metadata
Show full item recordCitation
Nazri, A., & Lio, P. (2012). Investigating meta-approaches for reconstructing gene networks in a mammalian cellular context.. PLoS One, 7 (1), e28713. https://doi.org/10.1371/journal.pone.0028713
Abstract
The output of state-of-the-art reverse-engineering methods for biological networks is often based on the fitting of a mathematical model to the data. Typically, different datasets do not give single consistent network predictions but rather an ensemble of inconsistent networks inferred under the same reverse-engineering method that are only consistent with the specific experimentally measured data. Here, we focus on an alternative approach for combining the information contained within such an ensemble of inconsistent gene networks called meta-analysis, to make more accurate predictions and to estimate the reliability of these predictions. We review two existing meta-analysis approaches; the Fisher transformation combined coefficient test (FTCCT) and Fisher's inverse combined probability test (FICPT); and compare their performance with five well-known methods, ARACNe, Context Likelihood or Relatedness network (CLR), Maximum Relevance Minimum Redundancy (MRNET), Relevance Network (RN) and Bayesian Network (BN). We conducted in-depth numerical ensemble simulations and demonstrated for biological expression data that the meta-analysis approaches consistently outperformed the best gene regulatory network inference (GRNI) methods in the literature. Furthermore, the meta-analysis approaches have a low computational complexity. We conclude that the meta-analysis approaches are a powerful tool for integrating different datasets to give more accurate and reliable predictions for biological networks.
Keywords
Animals, Area Under Curve, Bayes Theorem, Breast Neoplasms, Cells, Colorectal Neoplasms, Computational Biology, Computer Simulation, Databases, Genetic, Female, Gene Regulatory Networks, Humans, Mammals, Meta-Analysis as Topic, Reproducibility of Results, Software
Identifiers
External DOI: https://doi.org/10.1371/journal.pone.0028713
This record's URL: https://www.repository.cam.ac.uk/handle/1810/280446
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