ArticlePloS one2012
An ensemble classifier for eukaryotic protein subcellular location prediction using gene ontology categories and amino acid hydrophobicity.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
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- Quantitative Proteomics Analysis of Lettuce (International journal of molecular sciences · 2018Article
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- Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes.BMC bioinformatics · 2016Article
- Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins.BMC bioinformatics · 2016Article
- Protein Remote Homology Detection Based on an Ensemble Learning Approach.BioMed research international · 2016Article
- An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics.International journal of molecular sciences · 2015Article
- JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method.BioMed research international · 2015Article
- Article
- Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM.BMC bioinformatics · 2014Article
- An ensemble method approach to investigate kinase-specific phosphorylation sites.International journal of nanomedicine · 2014Article
- HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.PloS one · 2014Article
- Article
- Article
- SubMito-PSPCP: predicting protein submitochondrial locations by hybridizing positional specific physicochemical properties with pseudoamino acid compositions.BioMed research international · 2013Article
- Article
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7 authors.
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Abstract
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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