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URL:
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http://bioinformatics.ustc.edu.cn/LOCSVMPSI/LOCSVMPSI.php
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What you can do:
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Predict eukaryotic protein subcellular localization.
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Highlights:
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- This method 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.
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Keywords:
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- eukaryotic protein subcellular localization
- eukaryotic protein subcellular localization prediction tool
- protein localization prediction tool
- protein localization prediction tool
- protein sub-cellular localization prediction tool
- protein localizations
- Nuclear Localization Signal
- Nuclear Localization Signal prediction tool
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Literature and Tutorials:
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PubMed Link: LOCSVMPSI -- a web server for subcellular localization of eukaryotic proteins using SVM and profile of PSI-BLAST
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