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A critical analysis of the combined usage of protein localization prediction methods: Increasing the number of independent data sets can reduce the accuracy of predicted mitochondrial localization

A critical analysis of the combined usage of protein localization prediction methods: Increasing the number of independent data sets can reduce the accuracy of predicted mitochondrial localization
A critical analysis of the combined usage of protein localization prediction methods: Increasing the number of independent data sets can reduce the accuracy of predicted mitochondrial localization
In the absence of a comprehensive experimentally derived mitochondrial proteome, several bioinformatic approaches have been developed to aid the identification of novel mitochondrial disease genes within mapped nuclear genetic loci. Often, many classifiers are combined to increase the sensitivity and specificity of the predictions. Here we show that the greatest sensitivity and specificity are obtained by using a combination of seven carefully selected classifiers. We also show that increasing the number of independent prediction methods can paradoxically decrease the accuracy of predicting mitochondrial localization. This approach will help to accelerate the identification of new mitochondrial disease genes by providing a principled way for the selection for combination of appropriate prediction methods of mitochondrial localization of proteins.
mitochondria, proteome, bioinformatics, oxidative phosphorylation, mitochondrial disease
1567-7249
444-449
Lythgow, Kieren T.
3aeb00d0-0d35-4648-a215-3bafc85205af
Hudson, Gavin
cbad491d-cc7f-4bea-b214-b6752151fc13
Andras, Peter
e4f60324-9221-4e9a-b3d7-b9541eeb8802
Chinnery, Patrick F
87789d1a-5265-4815-9f11-194ed9b4ad94
Lythgow, Kieren T.
3aeb00d0-0d35-4648-a215-3bafc85205af
Hudson, Gavin
cbad491d-cc7f-4bea-b214-b6752151fc13
Andras, Peter
e4f60324-9221-4e9a-b3d7-b9541eeb8802
Chinnery, Patrick F
87789d1a-5265-4815-9f11-194ed9b4ad94

Lythgow, Kieren T., Hudson, Gavin, Andras, Peter and Chinnery, Patrick F (2011) A critical analysis of the combined usage of protein localization prediction methods: Increasing the number of independent data sets can reduce the accuracy of predicted mitochondrial localization. Mitochondrion, 11 (3), 444-449. (doi:10.1016/j.mito.2010.12.016). (PMID:21195798)

Record type: Article

Abstract

In the absence of a comprehensive experimentally derived mitochondrial proteome, several bioinformatic approaches have been developed to aid the identification of novel mitochondrial disease genes within mapped nuclear genetic loci. Often, many classifiers are combined to increase the sensitivity and specificity of the predictions. Here we show that the greatest sensitivity and specificity are obtained by using a combination of seven carefully selected classifiers. We also show that increasing the number of independent prediction methods can paradoxically decrease the accuracy of predicting mitochondrial localization. This approach will help to accelerate the identification of new mitochondrial disease genes by providing a principled way for the selection for combination of appropriate prediction methods of mitochondrial localization of proteins.

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More information

Published date: May 2011
Keywords: mitochondria, proteome, bioinformatics, oxidative phosphorylation, mitochondrial disease

Identifiers

Local EPrints ID: 192203
URI: http://eprints.soton.ac.uk/id/eprint/192203
ISSN: 1567-7249
PURE UUID: 9eee8f89-bb9e-4b35-9f30-63015aa36878

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Date deposited: 30 Jun 2011 12:42
Last modified: 08 Nov 2021 19:23

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Contributors

Author: Kieren T. Lythgow
Author: Gavin Hudson
Author: Peter Andras
Author: Patrick F Chinnery

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