ArticleNucleic acids research2005
LOCSVMPSI: a web server for subcellular localization of eukaryotic proteins using SVM and profile of PSI-BLAST.
Article in Nucleic acids research, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.
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59 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.Briefings in bioinformatics · 2019Pooled it
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- FEPS: A Tool for Feature Extraction from Protein Sequence.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian.Scientific reports · 2020Article
- 2lpiRNApred: a two-layered integrated algorithm for identifying piRNAs and their functions based on LFE-GM feature selection.RNA biology · 2020Article
- Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA.BMC bioinformatics · 2020Article
- Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method.Scientific reports · 2019Article
- A Brief History of Protein Sorting Prediction.The protein journal · 2019Review
- Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks.Iranian journal of biotechnology · 2018Article
- MDD-carb: a combinatorial model for the identification of protein carbonylation sites with substrate motifs.BMC systems biology · 2017Article
- MDD-Palm: Identification of protein S-palmitoylation sites with substrate motifs based on maximal dependence decomposition.PloS one · 2017Article
- UbiSite: incorporating two-layered machine learning method with substrate motifs to predict ubiquitin-conjugation site on lysines.BMC systems biology · 2016Article
- Article
- GSHSite: exploiting an iteratively statistical method to identify s-glutathionylation sites with substrate specificity.PloS one · 2015Article
- Protein fold recognition using geometric kernel data fusion.Bioinformatics (Oxford, England) · 2014Article
- PSNO: predicting cysteine S-nitrosylation sites by incorporating various sequence-derived features into the general form of Chou's PseAAC.International journal of molecular sciences · 2014Article
- Characterization and identification of protein O-GlcNAcylation sites with substrate specificity.BMC bioinformatics · 2014Article
- Incorporating significant amino acid pairs and protein domains to predict RNA splicing-related proteins with functional roles.Journal of computer-aided molecular design · 2014Article
- Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles.Bioinformatics (Oxford, England) · 2013Article
- PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif.Nucleic acids research · 2013Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Subcellular location of a protein is one of the key functional characters as proteins must be localized correctly at the subcellular level to have normal biological function. In this paper, a novel method named LOCSVMPSI has been introduced, which is based on the support vector machine (SVM) and the position-specific scoring matrix generated from profiles of PSI-BLAST. With a jackknife test on the RH2427 data set, LOCSVMPSI achieved a high overall prediction accuracy of 90.2%, which is higher than the prediction results by SubLoc and ESLpred on this data set. In addition, prediction performance of LOCSVMPSI was evaluated with 5-fold cross validation test on the PK7579 data set and the prediction results were consistently better than the previous method based on several SVMs using composition of both amino acids and amino acid pairs. Further test on the SWISSPROT new-unique data set showed that LOCSVMPSI also performed better than some widely used prediction methods, such as PSORTII, TargetP and LOCnet. All these results indicate that LOCSVMPSI is a powerful tool for the prediction of eukaryotic protein subcellular localization. An online web server (current version is 1.3) based on this method has been developed and is freely available to both academic and commercial users, which can be accessed by at http://Bioinformatics.ustc.edu.cn/LOCSVMPSI/LOCSVMPSI.php.
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