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A large-scale evaluation of computational protein function prediction.

Radivojac, Predrag, Clark, Wyatt T., Oron, Tal Ronnen, Schnoes, Alexandra M., Wittkop, Tobias, Sokolov, Artem, Graim, Kiley, Funk, Christopher, Verspoor, Karin, Ben-Hur, Asa, and others. (2013) A large-scale evaluation of computational protein function prediction. Nature Methods, 10 (3). pp. 221-227. ISSN 1548-7091. (doi:10.1038/nmeth.2340) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1038/nmeth.2340

Abstract

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

Item Type: Article
DOI/Identification number: 10.1038/nmeth.2340
Subjects: Q Science
Divisions: Faculties > Sciences > School of Biosciences
Depositing User: Mark Wass
Date Deposited: 06 Jun 2013 13:25 UTC
Last Modified: 29 May 2019 10:16 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/34180 (The current URI for this page, for reference purposes)
Wass, Mark N.: https://orcid.org/0000-0001-5428-6479
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