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An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity

With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper...

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Detalles Bibliográficos
Autores principales: Li, Liqi, Zhang, Yuan, Zou, Lingyun, Li, Changqing, Yu, Bo, Zheng, Xiaoqi, Zhou, Yue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3268814/
https://www.ncbi.nlm.nih.gov/pubmed/22303481
http://dx.doi.org/10.1371/journal.pone.0031057
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author Li, Liqi
Zhang, Yuan
Zou, Lingyun
Li, Changqing
Yu, Bo
Zheng, Xiaoqi
Zhou, Yue
author_facet Li, Liqi
Zhang, Yuan
Zou, Lingyun
Li, Changqing
Yu, Bo
Zheng, Xiaoqi
Zhou, Yue
author_sort Li, Liqi
collection PubMed
description With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict subcellular locations of eukaryotic proteins.
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spelling pubmed-32688142012-02-02 An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity Li, Liqi Zhang, Yuan Zou, Lingyun Li, Changqing Yu, Bo Zheng, Xiaoqi Zhou, Yue PLoS One Research Article With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict subcellular locations of eukaryotic proteins. Public Library of Science 2012-01-30 /pmc/articles/PMC3268814/ /pubmed/22303481 http://dx.doi.org/10.1371/journal.pone.0031057 Text en Li et al. http://creativecommons.org/licenses/by/4.0/ 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 author and source are properly credited.
spellingShingle Research Article
Li, Liqi
Zhang, Yuan
Zou, Lingyun
Li, Changqing
Yu, Bo
Zheng, Xiaoqi
Zhou, Yue
An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity
title An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity
title_full An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity
title_fullStr An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity
title_full_unstemmed An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity
title_short An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity
title_sort ensemble classifier for eukaryotic protein subcellular location prediction using gene ontology categories and amino acid hydrophobicity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3268814/
https://www.ncbi.nlm.nih.gov/pubmed/22303481
http://dx.doi.org/10.1371/journal.pone.0031057
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