"Guilt by Association" Is the Exception Rather Than the Rule in Gene Networks

Gillis, J., Pavlidis, P. (2012) "Guilt by Association" Is the Exception Rather Than the Rule in Gene Networks. PLoS Computational Biology, 8 (3). ISSN 1553-734X

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URL: http://www.ncbi.nlm.nih.gov/pubmed/22479173
DOI: 10.1371/journal.pcbi.1002444

Abstract

Gene networks are commonly interpreted as encoding functional information in their connections. An extensively validated principle called guilt by association states that genes which are associated or interacting are more likely to share function. Guilt by association provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. In this work, we show that functional information within gene networks is typically concentrated in only a very few interactions whose properties cannot be reliably related to the rest of the network. In effect, the apparent encoding of function within networks has been largely driven by outliers whose behaviour cannot even be generalized to individual genes, let alone to the network at large. While experimentalist-driven analysis of interactions may use prior expert knowledge to focus on the small fraction of critically important data, large-scale computational analyses have typically assumed that high-performance cross-validation in a network is due to a generalizable encoding of function. Because we find that gene function is not systemically encoded in networks, but dependent on specific and critical interactions, we conclude it is necessary to focus on the details of how networks encode function and what information computational analyses use to extract functional meaning. We explore a number of consequences of this and find that network structure itself provides clues as to which connections are critical and that systemic properties, such as scale-free-like behaviour, do not map onto the functional connectivity within networks.

Item Type: Paper
Uncontrolled Keywords: protein-protein interactions interaction database coexpression networks functional landscape function prediction by-association genomic data data sets yeast expression
Subjects: bioinformatics
bioinformatics > computational biology
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function
CSHL Authors:
Communities: CSHL labs > Gillis Lab
Depositing User: Matt Covey
Date Deposited: 30 Jan 2013 21:47
Last Modified: 06 Apr 2015 15:09
PMCID: PMC3315453
Related URLs:
URI: http://repository.cshl.edu/id/eprint/26947

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