The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Support vector machine approach for protein subcellular localization prediction
Abstract
Motivation: 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. Results: 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. Availability: A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/. Contact: sunzhr@mail.tsinghua.edu.cn; huasj00@mails.tsinghua.edu.cn Supplementary information: Supplementary material is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/. * To whom correspondence should be addressed.
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Yu, C., Lin, C., & Hwang, J. (2004) · Protein Science · doi:10.1110/ps.03479604 · Cited by 916
Rao, H. B., Zhu, F., Yang, G. B., Li, Z. R., & Chen, Y. Z. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr284 · Cited by 359
Antimicrobial Peptides: An Update on Classifications and Databases
Bin Hafeez, A., Jiang, X., Bergen, P. J., & Zhu, Y. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222111691 · Cited by 341
Proteomics in gram‐negative bacterial outer membrane vesicles
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Metadata
- Authors
- Hua, S., & Sun, Z.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2001-08-01
- Volume
- 17
- Issue
- 8
- Pages
- 721-728
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2160979370
- Cited by
- 854
- References
- 29
- FWCI
- 9.23
- Type
- journal article
- Language
- en
- Open access
- closed
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