Abstract
A novel approach CE-Ploc is proposed for predicting protein subcellular locations by exploiting diversity both in feature and decision spaces. The diversity in a sequence of feature spaces is exploited using hydrophobicity and hydrophilicity of amphiphilic pseudo amino acid composition and a specific learning mechanism. Diversity in learning mechanisms is exploited by fusion of classifiers that are based on different learning mechanisms. Significant improvement in prediction performance is observed using jackknife and independent dataset tests.
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References
Chou KC, Shen HB (2006) Predicting protein subcellular location by fusing multiple classifiers. J Cell Biochem 99:517–527. doi:10.1002/jcb.20879
Khan MF, Mujahid A, Khan A, Bangash A (2007) Prediction of protein sub-cellular localization through weighted combination of classifiers. International Conference on Electrical Engineering, ICEE
Khan A, Khan MF, Choi TS (2008) Proximity based GPCRs prediction in transform domain. Biochem Biophys Res Commun 371:411–415. doi:10.1016/j.bbrc.2008.04.074
Kuncheva LI, Whitaker CJ (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51:181–207. doi:10.1023/A:1022859003006
Nanni L, Lumini A (2006) An ensemble of K-local hyperplane for predicting protein–protein interactions. Bioinformatics 22:1207–1210. doi:10.1093/bioinformatics/btl055
Nanni L, Lumini A (2007) Ensemblator: an ensemble of classifiers for reliable classification of biological data. Pattern Recognit Lett 28:622–630. doi:10.1016/j.patrec.2006.10.012
Nanni L, Lumini A (2008a) An ensemble of support vector machines for predicting the membrane protein type directly from the amino acid sequence. Amino Acids 35:573–580. doi:10.1007/s00726-008-0083-0
Nanni L, Lumini A (2008b) A genetic approach for building different alphabets for peptide and protein classification. BMC Bioinformatics 9:45. doi:10.1186/1471-2105-9-45
Nanni L, Lumini A (2008c) Using ensemble of classifiers in Bioinformatics. In: Vogel HPAM (ed) Machine learning research progress. Nova Science Publishers Inc, New York
Shen Y, Burger G (2007) ‘Unite and conquer’: enhanced prediction of protein subcellular localization by integrating multiple specialized tools. BMC Bioinformatics 8:420. doi:10.1186/1471-2105-8-420
Shen HB, Chou KC (2007a) Gpos-PLoc: an ensemble classifier for predicting subcellular localization of Gram-positive bacterial proteins. Protein Eng Des Sel 20:39–46. doi:10.1093/protein/gzl053
Shen HB, Chou KC (2007b) Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites. Biochem Biophys Res Commun 355:1006–1011. doi:10.1016/j.bbrc.2007.02.071
Shen HB, Chou KC (2007c) Virus-PLoc: a fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells. Biopolymers 85:233–240. doi:10.1002/bip.20640
Shen HB, Yang J, Chou KC (2007) Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction. Amino Acids 33:57–67. doi:10.1007/s00726-006-0478-8
Shi JY, Zhang SW, Pan Q, Cheng YM, Xie J (2007) Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition. Amino Acids 33:69–74. doi:10.1007/s00726-006-0475-y
Yu C-S, Lin C-J, Hwang J-K (2004) Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci 13:1402–1406. doi:10.1110/ps.03479604
Acknowledgments
This work was supported by the Bio Imaging Research Center at Gwangju Institute of Science and Technology (GIST), South Korea.
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Khan, A., Majid, A. & Choi, TS. Predicting protein subcellular location: exploiting amino acid based sequence of feature spaces and fusion of diverse classifiers. Amino Acids 38, 347–350 (2010). https://doi.org/10.1007/s00726-009-0238-7
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DOI: https://doi.org/10.1007/s00726-009-0238-7