Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update

Pavlidis, P., Gillis, J. (2013) Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update. F1000 Research, 2. p. 230. ISSN 20461402 (ISSN)

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URL: http://www.ncbi.nlm.nih.gov/pubmed/24715959
DOI: 10.12688/f1000research.2-230.v1

Abstract

In an opinion published in 2012, we reviewed and discussed our studies of how gene network-based guilt-by-association (GBA) is impacted by confounds related to gene multifunctionality. We found such confounds account for a significant part of the GBA signal, and as a result meaningfully evaluating and applying computationally-guided GBA is more challenging than generally appreciated. We proposed that effort currently spent on incrementally improving algorithms would be better spent in identifying the features of data that do yield novel functional insights. We also suggested that part of the problem is the reliance by computational biologists on gold standard annotations such as the Gene Ontology. In the year since, there has been continued heavy activity in GBA-based research, including work that contributes to our understanding of the issues we raised. Here we provide a review of some of the most relevant recent work, or which point to new areas of progress and challenges.

Item Type: Paper
Subjects: 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: 19 Dec 2014 15:33
Last Modified: 19 Dec 2014 15:33
PMCID: PMC3962002
Related URLs:
URI: http://repository.cshl.edu/id/eprint/30983

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