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
| Autor: | Gavin Hudson, Peter Andras, Patrick F. Chinnery, Kieren Lythgow |
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| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Proteome
Bioinformatics Mitochondrial disease Computational biology Biology Mitochondrion Sensitivity and Specificity Article Mitochondrial Proteins Mitochondria Proteome Bioinformatics Oxidative phosphorylation Mitochondrial disease 03 medical and health sciences 0302 clinical medicine medicine Humans Oxidative phosphorylation Sensitivity (control systems) Gene Molecular Biology Selection (genetic algorithm) 030304 developmental biology Genetics 0303 health sciences Computational Biology Cell Biology medicine.disease Protein subcellular localization prediction Mitochondria Identification (information) Protein Transport Molecular Medicine 030217 neurology & neurosurgery |
| Zdroj: | Mitochondrion |
| ISSN: | 1872-8278 1567-7249 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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