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• W2160979370 abstract "Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences.In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals.A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.Supplementary material is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/." @default.
• W2160979370 created "2016-06-24" @default.
• W2160979370 creator A5026291475 @default.
• W2160979370 creator A5085077889 @default.
• W2160979370 date "2001-08-01" @default.
• W2160979370 modified "2023-10-10" @default.
• W2160979370 title "Support vector machine approach for protein subcellular localization prediction" @default.
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• W2160979370 doi "https://doi.org/10.1093/bioinformatics/17.8.721" @default.
• W2160979370 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/11524373" @default.
• W2160979370 hasPublicationYear "2001" @default.
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