Literature DB >> 15546939

Probabilities of spurious connections in gene networks: application to expression time series.

David R Bickel1.   

Abstract

MOTIVATION: The reconstruction of gene networks from gene-expression microarrays is gaining popularity as methods improve and as more data become available. The reliability of such networks could be judged by the probability that a connection between genes is spurious, resulting from chance fluctuations rather than from a true biological relationship.
RESULTS: Unlike the false discovery rate and positive false discovery rate, the decisive false discovery rate (dFDR) is exactly equal to a conditional probability without assuming independence or the randomness of hypothesis truth values. This property is useful not only in the common application to the detection of differential gene expression, but also in determining the probability of a spurious connection in a reconstructed gene network. Estimators of the dFDR can estimate each of three probabilities: (1) The probability that two genes that appear to be associated with each other lack such association. (2) The probability that a time ordering observed for two associated genes is misleading. (3) The probability that a time ordering observed for two genes is misleading, either because they are not associated or because they are associated without a lag in time. The first probability applies to both static and dynamic gene networks, and the other two only apply to dynamic gene networks.

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Year:  2004        PMID: 15546939     DOI: 10.1093/bioinformatics/bti140

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  8 in total

1.  Predictive Integration of Gene Ontology-Driven Similarity and Functional Interactions.

Authors:  Francisco Azuaje; Haiying Wang; Huiru Zheng; Olivier Bodenreider; Alban Chesneau
Journal:  Proc IEEE Int Conf Data Min       Date:  2006-12

2.  Inference of gene regulatory networks using time-series data: a survey.

Authors:  Chao Sima; Jianping Hua; Sungwon Jung
Journal:  Curr Genomics       Date:  2009-09       Impact factor: 2.236

3.  Nonparametric identification of regulatory interactions from spatial and temporal gene expression data.

Authors:  Anil Aswani; Soile V E Keränen; James Brown; Charless C Fowlkes; David W Knowles; Mark D Biggin; Peter Bickel; Claire J Tomlin
Journal:  BMC Bioinformatics       Date:  2010-08-04       Impact factor: 3.169

Review 4.  Inferring cellular networks--a review.

Authors:  Florian Markowetz; Rainer Spang
Journal:  BMC Bioinformatics       Date:  2007-09-27       Impact factor: 3.169

5.  Comprehensive analysis of gene-environmental interactions with temporal gene expression profiles in Pseudomonas aeruginosa.

Authors:  Kangmin Duan; William M McCullough; Michael G Surette; Tony Ware; Jiuzhou Song
Journal:  PLoS One       Date:  2012-04-27       Impact factor: 3.240

6.  Gene network reconstruction from transcriptional dynamics under kinetic model uncertainty: a case for the second derivative.

Authors:  David R Bickel; Zahra Montazeri; Pei-Chun Hsieh; Mary Beatty; Shai J Lawit; Nicholas J Bate
Journal:  Bioinformatics       Date:  2009-02-13       Impact factor: 6.937

7.  Clustering and Differential Alignment Algorithm: Identification of Early Stage Regulators in the Arabidopsis thaliana Iron Deficiency Response.

Authors:  Alexandr Koryachko; Anna Matthiadis; Durreshahwar Muhammad; Jessica Foret; Siobhan M Brady; Joel J Ducoste; James Tuck; Terri A Long; Cranos Williams
Journal:  PLoS One       Date:  2015-08-28       Impact factor: 3.240

Review 8.  Comparative study of computational methods for reconstructing genetic networks of cancer-related pathways.

Authors:  Nafiseh Sedaghat; Takumi Saegusa; Timothy Randolph; Ali Shojaie
Journal:  Cancer Inform       Date:  2014-09-21
  8 in total

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