ProLoc: Prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features

BioSystems

Volumn 90, Issue 2, 2007, Pages 573-581

  Huang, Wen Lin ^a   Tung, Chun Wei ^b   Huang, Hui Ling ^c   Hwang, Shiow Fen ^a   Ho, Shinn Ying ^b  

^a FENG CHIA UNIVERSITY   (Taiwan)

^b NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^c JINWEN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Amino acid composition; Genetic algorithm; k Nearest neighbor; Physicochemical property; Prediction; Subnuclear localization; Support vector machine

Indexed keywords

AMINO ACID; GENETIC ALGORITHM; NEAREST NEIGHBOR ANALYSIS; PEPTIDE; PREDICTION; PROTEIN;

ACCURACY; AMINO ACID COMPOSITION; ARTICLE; CELL COMPARTMENTALIZATION; CLASSIFIER; GENETIC ALGORITHM; HERITABILITY; INTRANUCLEAR SPACE; OUTCROSSING; PHYSICAL CHEMISTRY; PREDICTION; PROTEIN LOCALIZATION; SUPPORT VECTOR MACHINE; VALIDATION PROCESS;

EID: 34548458988     PISSN: 03032647     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystems.2007.01.001     Document Type: Article

References (28)

1. Bhasin M., Garg A., and Raghava G.P.S. PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 21 (2005) 2522-2524
2. Bhasin M., and Raghava G.P.S. ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST. Nucleic Acids Res. 32 (2004) W414-W419
3. Brotherton T.W., Simpson P.K., Fogel D.B., and Pollard T. Classifier design using evolutionary programming. In: Sebald A.V., and Fogel L.J. (Eds). Proceedings for the Third Annual Conference on Evolutionary Programming (1994), World Scientific, Singapore 68-75
4. Brendel V. PROSET-a fast procedure to create non-redundant sets of protein sequences. Math. Comput. Modell. 16 (1992) 37-43
5. Cai Y.D., Liu X.J., Xu X.B., and Chou K.C. Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect. J. Cell. Biochem. 84 (2002) 343-348
6. Dellaire G., Farrall R., and Bickmore W.A. The Nuclear Protein Database (NPD): sub-nuclear localisation and functional annotation of the nuclear proteome. Nucleic Acids Res. 31 (2003) 328-330
7. Gardy J.L., Laird M.R., Chen F., Rey S., Walsh C.J., Ester M., and Brinkman F.S.L. PSORTb v.2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics 21 (2005) 617-623
8. Heidi G.E.S., Gail K.M., Kathryn N., Lisa V.F., Rachel F., Graham D., Javier F.C., and Wendy A.B. Large-scale identification of mammalian proteins localized to nuclear sub-compartments. Hum. Mol. Genet. 10 (2001) 1995-2011
9. Ho S.-Y., Hsieh C.-H., Chen H.-M., and Huang H.-L. Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis. BioSystems 85 (2006) 165-176
10. Ho S.-Y., Chen J.-H., and Huang M.-H. Inheritable genetic algorithm for biobjective 0/1 combinatorial optimization problems and its applications. IEEE Trans. Syst. Man Cybern.-Part B 34 (2004) 609-620
11. Ho S.-Y., Liu C.-C., and Liu S. Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm. Pattern Recognit. Lett. 23 (2002) 1495-1503
12. Ho S.-Y., Shu L.-S., and Chen J.-H. Intelligent evolutionary algorithms for large parameter optimization problems. IEEE Trans. Evol. Comput. 8 (2004) 522-541
13. Hua S., and Sun Z. Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17 (2001) 721-728
14. Joachims T. Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms (2002), Kluwer Academic Publishers
15. Kawashima S., and Kanehisa M. AAindex: amino acid index database. Nucleic Acids Res. 28 (2000) 374
16. Lei Z., and Dai Y. An SVM-based system for predicting protein subnuclear localizations. BMC Bioinformatics 6 (2005) 291-298
17. Li T., Zhang C., and Ogihara M. A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 20 (2004) 2429-2437
18. Nair R., and Rost B. Mimicking cellular sorting improves prediction of subcellular localization. J. Mol. Biol. 348 (2005) 85-100
19. Nakai K., and Horton P. PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization. Trends Biochem. Sci. 24 (1999) 34-35
20. Ooi C.H., and Tan P. Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics 19 (2003) 37-44
21. Phair R.D., and Misteli T. High mobility of proteins in the mammalian cell nucleus. Nature 404 (2000) 604-609
22. Sarda D., Chua G., Li K.-B., and Krishnan A. pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties. BMC Bioinformatics 6 (2005) 152-163
23. Shen H.B., and Chou K.C. Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. Biochem. Biophys. Res. Commun. 337 (2005) 752-756
24. Spector D.L. Nuclear domains. J. Cell Sci. 114 (2001) 2891-2893
25. Stone M. Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. 36 (1974) 111-147
26. Sun Z., Bebis G., and Miller R. Object detection using feature subset selection. Pattern Recognit. 37 (2004) 2165-2176
27. Szafron D., Lu P., Greiner R., Wishart D.S., Poulin B., Eisner R., Lu Z., Anvik J., Macdonell C., Fyshe A., et al. Proteome analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations. Nucleic Acids Res. 32 (2004) W365-W371
28. Yu C.S., Lin C.J., and Hwang J.K. Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci. 13 (2004) 1402-1406