標題: Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions
作者: Yu, CS
Lin, CJ
Hwang, JK
生物科技學系
生物資訊及系統生物研究所
Department of Biological Science and Technology
Institude of Bioinformatics and Systems Biology
關鍵字: subcellular localization;support vector machine;Gram-negative bacteria;machine-learning method;proteome;genome;n-peptide compositions
公開日期: 1-May-2004
摘要: Gram-negative bacteria have five major subcellular localization sites: the cytoplasm, the periplasm, the inner membrane, the outer membrane, and the extracellular space. The subcellular location of a protein can provide valuable information about its function. With the rapid increase of sequenced genomic data, the need for an automated and accurate to predict subcellular localization becomes increasingly important. We present an approach to predict subcellular localization for Gram-negative bacteria. This method uses the support vector machines trained by multiple feature vectors based on n-peptide compositions. For a standard data set comprising 1443 proteins, the overall prediction accuracy reaches 89%, which, to the best of our knowledge, is the highest prediction rate ever reported. Our prediction is 14% higher than that of the recently developed multimodular PSORT-B. Because of its simplicity, this approach can be easily extended to other organisms and should be a useful tool for the high-throughput and large-scale analysis of proteomic and genomic data.
URI: http://dx.doi.org/10.1110/ps.03479604
http://hdl.handle.net/11536/26836
ISSN: 0961-8368
DOI: 10.1110/ps.03479604
期刊: PROTEIN SCIENCE
Volume: 13
Issue: 5
起始頁: 1402
結束頁: 1406
Appears in Collections:Articles