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Title: Predicting protein function by machine learning on amino acid sequences – a critical evaluation
Authors: Al-Shahib, A
Breitling, R
Gilbert, D
Keywords: Newly discovered proteins;Amino acid sequence;Post-genomic computational biology;Machine learning classifiers
Issue Date: 2007
Publisher: BioMed Central Ltd
Citation: BMC Genomics, 8: 78, 2007
Abstract: Background: Predicting the function of newly discovered proteins by simply inspecting their amino acid sequence is one of the major challenges of post-genomic computational biology, especially when done without recourse to experimentation or homology information. Machine learning classifiers are able to discriminate between proteins belonging to different functional classes. Until now, however, it has been unclear if this ability would be transferable to proteins of unknown function, which may show distinct biases compared to experimentally more tractable proteins. Results: Here we show that proteins with known and unknown function do indeed differ significantly. We then show that proteins from different bacterial species also differ to an even larger and very surprising extent, but that functional classifiers nonetheless generalize successfully across species boundaries. We also show that in the case of highly specialized proteomes classifiers from a different, but more conventional, species may in fact outperform the endogenous species-specific classifier. Conclusion: We conclude that there is very good prospect of successfully predicting the function of yet uncharacterized proteins using machine learning classifiers trained on proteins of known function.
Description: Copyright @ 2007 Al-Shahib et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ISSN: 1471-2164
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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