ArticleBMC bioinformatics2004
Esub8: a novel tool to predict protein subcellular localizations in eukaryotic organisms.
Article in BMC bioinformatics, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment.Journal of integrative bioinformatics · 2020Article
- Going from where to why--interpretable prediction of protein subcellular localization.Bioinformatics (Oxford, England) · 2010Article
- MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction.BMC bioinformatics · 2009Article
- ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins.BMC bioinformatics · 2008Article
- Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence.BMC bioinformatics · 2006Article
- Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis.Genomics, proteomics & bioinformatics · 2006Article
- Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information.BMC bioinformatics · 2006Article
- pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties.BMC bioinformatics · 2005Article
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4 authors.
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Abstract
backgroundSubcellular localization of a new protein sequence is very important and fruitful for understanding its function. As the number of new genomes has dramatically increased over recent years, a reliable and efficient system to predict protein subcellular location is urgently needed.
resultsEsub8 was developed to predict protein subcellular localizations for eukaryotic proteins based on amino acid composition. In this research, the proteins are classified into the following eight groups: chloroplast, cytoplasm, extracellular, Golgi apparatus, lysosome, mitochondria, nucleus and peroxisome. We know subcellular localization is a typical classification problem; consequently, a one-against-one (1-v-1) multi-class support vector machine was introduced to construct the classifier. Unlike previous methods, ours considers the order information of protein sequences by a different method. Our method is tested in three subcellular localization predictions for prokaryotic proteins and four subcellular localization predictions for eukaryotic proteins on Reinhardt's dataset. The results are then compared to several other methods. The total prediction accuracies of two tests are both 100% by a self-consistency test, and are 92.9% and 84.14% by the jackknife test, respectively. Esub8 also provides excellent results: the total prediction accuracies are 100% by a self-consistency test and 87% by the jackknife test.
conclusionsOur method represents a different approach for predicting protein subcellular localization and achieved a satisfactory result; furthermore, we believe Esub8 will be a useful tool for predicting protein subcellular localizations in eukaryotic organisms.
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