標題: ProLoc: Prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features
作者: Huang, Wen-Lin
Tung, Chun-Wei
Huang, Hui-Ling
Hwang, Shiow-Fen
Ho, Shinn-Ying
生物資訊及系統生物研究所
Institude of Bioinformatics and Systems Biology
關鍵字: subnuclear localization;support vector machine;k-Nearest neighbor;prediction;amino acid composition;physicochemical property;genetic algorithm
公開日期: 1-九月-2007
摘要: Accurate prediction methods of protein subnuclear localizations rely on the cooperation between informative features and classifier design. Support vector machine (SVM) based learning methods are shown effective for predictions of protein subcellular and subnuclear localizations. This study proposes an evolutionary support vector machine (ESVM) based classifier with automatic selection from a large set of physicochernical composition (PCC) features to design an accurate system for predicting protein subnuclear localization, named ProLoc. ESVM using an inheritable genetic algorithm combined with SVM can automatically determine the best number m of PCC features and identify m out of 526 PCC features simultaneously. To evaluate ESVM, this study uses two datasets SNL6 and SNL9, which have 504 proteins localized in 6 subnuclear compartments and 370 proteins localized in 9 subnuclear compartments. Using a leave-one-out cross-validation, ProLoc utilizing the selected m = 33 and 28 PCC features has accuracies of 56.37% for SNL6 and 72.82% for SNL9, which are better than 51.4% for the SVM-based system using k-peptide composition features applied on SNL6, and 64.32% for an optimized evidence-theoretic k-nearest neighbor classifier utilizing pseudo amino acid composition applied on SNL9, respectively. (C) 2007 Elsevier Ireland Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.biosystems.2007.01.001
http://hdl.handle.net/11536/10348
ISSN: 0303-2647
DOI: 10.1016/j.biosystems.2007.01.001
期刊: BIOSYSTEMS
Volume: 90
Issue: 2
起始頁: 573
結束頁: 581
顯示於類別:期刊論文


文件中的檔案:

  1. 000250184500028.pdf