NS  Vol.2 No.10 , October 2010
Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms
ABSTRACT
Cell-PLoc 2.0 is a package of web-servers evolved from Cell-PLoc (Chou, K.C. & Shen, H.B., Nature Protocols, 2008, 2:153-162) by a top-down approach to improve the power for predicting subcellular localization of proteins in various organisms. It contains six predictors: Euk-mPLoc 2.0, Hum-mPLoc 2.0, Plant-mPLoc, Gpos-mPLoc, Gneg-mPLoc, and Virus-mPLoc, specialized for eukaryotic, human, plant, Gram- positive bacterial, Gram-negative bacterial, and virus proteins, respectively. Compared with Cell-PLoc, the predictors in the Cell-PLoc 2.0 have the following advantageous features: (1) they all have the capacity to deal with the multiplex proteins that can simultaneiously exist, or move between, two or more subcellular location sites; (2) no accession number is needed for the input of a query protein even if using the “high- level” GO (gene ontology) prediction engine; (3) the functional domain information and sequential evolution information are fused into the “ab initio” sequence-based prediction engine to enhance its accuracy. In this protocol, a step- to-step guide is provided for how to use the web server predictors in the Cell-PLoc 2.0 package, which is freely accessible to the public at http://www.csbio.sjtu.edu.cn/bioinf/Cell-PLoc-2/.

Cite this paper
Chou, K. and Shen, H. (2010) Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms. Natural Science, 2, 1090-1103. doi: 10.4236/ns.2010.210136.
References
[1]   Ehrlich, J.S., Hansen, M.D., Nelson, W.J. (2002) Spatio- temporal regulation of Rac1 localization and lamellipodia dynamics during epithelial cell-cell adhesion. Dev Cell, 3, 259-270.

[2]   Glory, E., Murphy, R.F. (2007) Automated subcellular location determination and high-throughput microscopy. Dev Cell, 12, 7-16.

[3]   Smith, C. (2008) Subcellular targeting of proteins and drugs. http://www.biocompare.com/Articles/TechnologySpotlight/976/Subcellular-Targeting-Of-Proteins-And-Drugs.html

[4]   Nakai, K., Kanehisa, M. (1991) Expert system for pre-dicting protein localization sites in Gram-negative bacteria. Proteins: Structure, Function and Genetics, 11, 95- 110.

[5]   Nakashima, H., Nishikawa, K. (1994) Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. Journal of Molecular Biology, 238, 54-61.

[6]   Cedano, J., Aloy, P., P'erez-Pons, J.A., Querol, E. (1997) Relation between amino acid composition and cellular location of proteins. Journal of Molecular Biology, 266, 594-600.

[7]   Nakai, K., Horton, P. (1999) PSORT: A program for de-tecting sorting signals in proteins and predicting their subcellular localization. Trends in Biochemical Science, 24, 34-36.

[8]   Chou, K.C., Elrod, D.W. (1998) Using discriminant func-tion for prediction of subcellular location of prokaryotic proteins. Biochemical and Biophysical Research Com-munications, 252, 63-68.

[9]   Reinhardt, A., Hubbard, T. (1998) Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Research, 26, 2230-2236.

[10]   Chou, K.C., Elrod, D.W. (1999) Protein subcellular loca-tion prediction. Protein Engineering, 12, 107-118.

[11]   Yuan, Z. (1999) Prediction of protein subcellular locations using Markov chain models. FEBS Letters, 451, 23-26.

[12]   Nakai, K. (2000) Protein sorting signals and prediction of subcellular localization. Advances in Protein Chemistry, 54, 277-344.

[13]   Murphy, R.F., Boland, M.V., Velliste, M. (2000) Towards a systematics for protein subcellular location: quantitative description of protein localization patterns and automated analysis of fluorescence microscope images. Proc. Int. Conf. Intell. Syst. Mol. Biol., 8, 251-259.

[14]   Chou, K.C. (2000) Review: Prediction of protein struc-tural classes and subcellular locations. Current Protein and Peptide Science, 1, 171-208.

[15]   Emanuelsson, O., Nielsen, H., Brunak, S., von Heijne, G. (2000) Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. Journal of Molecular Biology, 300, 1005-1016.

[16]   Chou, K.C. (2001) Prediction of protein cellular attributes using pseudo amino acid composition. PROTEINS: Structure, Function, and Genetics (Erratum: ibid., 2001, Vol.44, 60), 43, 246-255.

[17]   Feng, Z.P. (2001) Prediction of the subcellular location of prokaryotic proteins based on a new representation of the amino acid composition. Biopolymers, 58, 491-499.

[18]   Hua, S., Sun, Z. (2001) Support vector machine approach for protein subcellular localization prediction. Bioinfor-matics, 17, 721-728.

[19]   Feng, Z.P., Zhang, C.T. (2001) Prediction of the subcel-lular location of prokaryotic proteins based on the hy-drophobicity index of amino acids. Int. J. Biol. Macromol., 28, 255-261.

[20]   Feng, Z.P. (2002) An overview on predicting the subcel-lular location of a protein. In. Silico. Biol., 2, 291-303.

[21]   Chou, K.C., Cai, Y.D. (2002) Using functional domain composition and support vector machines for prediction of protein subcellular location. Journal of Biological Chemistry, 277, 45765-45769.

[22]   Zhou, G.P., Doctor, K. (2003) Subcellular location pre-diction of apoptosis proteins. Proteins: Structure, Func-tion, and Genetics, 50, 44-48.

[23]   Pan, Y.X., Zhang, Z.Z., Guo, Z.M., Feng, G.Y., Huang, Z.D., He, L. (2003) Application of pseudo amino acid composition for predicting protein subcellular location: Stochastic signal processing approach. Journal of Protein Chemistry, 22, 395-402.

[24]   Park, K.J., Kanehisa, M. (2003) Prediction of protein subcellular locations by support vector machines using compositions of amino acid and amino acid pairs. Bioin-formatics, 19, 1656-1663.

[25]   Gardy, J.L., Spencer, C., Wang, K., Ester, M., Tusnady, G.E., Simon, I., Hua, S., deFays, K., Lambert, C., Nakai, K., Brinkman, F.S. (2003) PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria. Nucleic Acids Research, 31, 3613-3617.

[26]   Huang, Y., Li, Y. (2004) Prediction of protein subcellular locations using fuzzy k-NN method. Bioinformatics, 20, 21-28.

[27]   Xiao, X., Shao, S., Ding, Y., Huang, Z., Huang, Y., Chou, K.C. (2005) Using complexity measure factor to predict protein subcellular location. Amino Acids, 28, 57-61.

[28]   Gao, Y., Shao, S.H., Xiao, X., Ding, Y.S., Huang, Y.S., Huang, Z.D., Chou, K.C. (2005) Using pseudo amino acid composition to predict protein subcellular location: approached with Lyapunov index, Bessel function, and Chebyshev filter. Amino Acids, 28, 373-376.

[29]   Lei, Z., Dai, Y. (2005) An SVM-based system for pre-dicting protein subnuclear localizations BMC. Bioinfor-matics, 6, 291.

[30]   Shen, H.B., Chou, K.C. (2005) Predicting protein sub-nuclear location with optimized evidence-theoretic K-earest classifier and pseudo amino acid composition. Biochem. Biophys. Res. Comm., 337, 752-756.

[31]   Garg, A., Bhasin, M., Raghava, G.P. (2005) Support vec-tor machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search. Journal of Biological Chemistry, 280, 14427-14432.

[32]   Matsuda, S., Vert, J.P., Saigo, H., Ueda, N., Toh, H., Akutsu, T. (2005) A novel representation of protein se-quences for prediction of subcellular location using sup-port vector machines. Protein Sci., 14, 2804-2813.

[33]   Gardy, J.L., Laird, M.R., Chen, F., Rey, S., Walsh, C.J., Ester, M., Brinkman, F.S. (2005) PSORTb v.2.0: ex-panded prediction of bacterial protein subcellular locali-zation and insights gained from comparative proteome analysis. Bioinformatics, 21, 617-623.

[34]   Gao, Q.B., Wang, Z.Z., Yan, C., Du, Y.H. (2005) Predic-tion of protein subcellular location using a combined feature of sequence. FEBS Letters, 579, 3444-3448.

[35]   Chou, K.C., Shen, H.B. (2006) Predicting protein sub-cellular location by fusing multiple classifiers. Journal of Cellular Biochemistry, 99, 517-527.

[36]   Guo, J., Lin, Y., Liu, X. (2006) GNBSL: A new integra-tive system to predict the subcellular location for Gram- egative bacteria proteins. Proteomics, 6, 5099-5105.

[37]   Xiao, X., Shao, S.H., Ding, Y.S., Huang, Z.D., Chou, K.C. (2006) Using cellular automata images and pseudo amino acid composition to predict protein subcellular lo-cation. Amino Acids, 30, 49-54.

[38]   Hoglund, A., Donnes, P., Blum, T., Adolph, H.W., Kohl-bacher, O. (2006) MultiLoc: prediction of protein sub-cellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition. Bioinfor-matics, 22, 1158-1165.

[39]   Lee, K., Kim, D.W., Na, D., Lee, K.H., Lee, D. (2006) PLPD: reliable protein localization prediction from im-balanced and overlapped datasets. Nucleic Acids Research, 34, 4655-4666.

[40]   Zhang, Z.H., Wang, Z.H., Zhang, Z.R., Wang, Y.X. (2006) A novel method for apoptosis protein subcellular locali-zation prediction combining encoding based on grouped weight and support vector machine. FEBS Letters, 580, 6169-6174.

[41]   Shi, J.Y., Zhang, S.W., Pan, Q., Cheng, Y.-M., Xie, J. (2007) Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo mino acid composition. Amino Acids, 33, 69-74.

[42]   Chen, Y.L., Li, Q.Z. (2007) Prediction of apoptosis pro-tein subcellular location using improved hybrid approach and pseudo amino acid composition. Journal of Theoret-ical Biology, 248, 377–381.

[43]   Chen, Y.L., Li, Q.Z. (2007) Prediction of the subcellular location of apoptosis proteins. Journal of Theoretical Bi-ology, 245, 775-783.

[44]   Mundra, P., Kumar, M., Kumar, K.K., Jayaraman, V.K., Kulkarni, B.D. (2007) Using pseudo amino acid compo-sition to predict protein subnuclear localization: Ap-proached with PSSM. Pattern Recognition Letters, 28, 1610-1615.

[45]   Emanuelsson, O., Brunak, S., von Heijne, G., Nielsen, H. (2007) Locating proteins in the cell using TargetP, SignalP and related tools. Nature Protocols, 2, 953-971.

[46]   Lin, H., Ding, H., Feng-Biao Guo, F.B., Zhang, A.Y., Huang, J. (2008) Predicting subcellular localization of mycobacterial proteins by using Chou’s pseudo amino acid composition. Protein & Peptide Letters, 15, 739-744.

[47]   Shi, J.Y., Zhang, S.W., Pan, Q., Zhou, G.P. (2008) Using Pseudo Amino Acid Composition to Predict Protein Subcellular Location: Approached with Amino Acid Composition Distribution. Amino Acids, 35, 321-327.

[48]   Li, F.M., Li, Q.Z. (2008) Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach. Protein & Peptide Letters, 15, 612-616.

[49]   Tantoso, E., Li, X.B. (2008) AAIndexLoc: Predicting Subcellular Localization of Proteins Based on a New Representation of Sequences Using Amino Acid Indices. Amino Acids, 35, 345-353.

[50]   Jiang, X., Wei, R., Zhang, T.L., Gu, Q. (2008) Using the concept of Chou’s pseudo amino acid composition to predict apoptosis proteins subcellular location: an ap-proach by approximate entropy. Protein & Peptide Letters, 15, 392-396.

[51]   Zhou, X.B., Chen, C., Li, Z.C., Zou, X.Y. (2008) Im-proved prediction of subcellular location for apoptosis proteins by the dual-layer support vector machine. Amino Acids, 35, 383-388.

[52]   Ding, Y.S., Zhang, T.L. (2008) Using Chou’s pseudo amino acid composition to predict subcellular localization of apoptosis proteins: an approach with immune genetic algorithm-based ensemble classifier. Pattern Recognition Letters, 29, 1887-1892.

[53]   Zhang, S.W., Zhang, Y.L., Yang, H.F., Zhao, C.H., Pan, Q. (2008) Using the concept of Chou’s pseudo amino acid composition to predict protein subcellular localization: an approach by incorporating evolutionary information and von Neumann entropies. Amino Acids, 34, 565-572.

[54]   Jin, Y., Niu, B., Feng, K.Y., Lu, W.C., Cai, Y.D., Li, G.Z. (2008) Predicting subcellular localization with AdaBoost learner. Protein & Peptide Letters, 15, 286-289.

[55]   Lin, H., Wang, H., Ding, H., Chen, Y.L., Li, Q.Z. (2009) Prediction of Subcellular Localization of Apoptosis Pro-tein Using Chou’s Pseudo Amino Acid Composition. Acta Biotheoretica, 57, 321-330.

[56]   Zhang, L., Liao, B., Li, D., Zhu, W. (2009) A novel re-presentation for apoptosis protein subcellular localization prediction using support vector machine. Journal of Theoretical Biology, 259, 361-365.

[57]   Zeng, Y.H., Guo, Y.Z., Xiao, R.Q., Yang, L., Yu, L.Z., Li, M.L. (2009) Using the augmented Chou’s pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. Journal of Theoretical Biology, 259, 366-72.

[58]   Du, P., Cao, S., Li, Y. (2009) SubChlo: predicting protein subchloroplast locations with pseudo-amino acid compo-sition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm. Journal of Theoretical Biology, 261, 330-335.

[59]   Cai, Y.D., He, J., Li, X., Feng, K., Lu, L., Kong, X., Lu, W. (2010) Predicting protein subcellular locations with feature selection and analysis. Protein Pept. Lett., 17, 464-472.

[60]   Millar, A.H., Carrie, C., Pogson, B., Whelan, J. (2009) Exploring the function-location nexus: using multiple lines of evidence in defining the subcellular location of plant proteins. Plant Cell, 21, 1625-1631.

[61]   Chou, K.C., Shen, H.B. (2008) Cell-PLoc: A package of Web servers for predicting subcellular localization of proteins in various organisms. Nature Protocols, 3, 153- 162.

[62]   Chou, K.C., Shen, H.B. (2007) Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. Journal of Proteome Research, 6, 1728-1734.

[63]   Shen, H.B., Chou, K.C. (2007) Hum-mPLoc: An ensem-ble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites. Biochemical and Biophysical Research Com-munications, 355, 1006-1011.

[64]   Chou, K.C., Shen, H.B. (2007) Large-scale plant protein subcellular location prediction. Journal of Cellular Bio-chemistry, 100, 665-678.

[65]   Shen, H.B., Chou, K.C. (2007) Gpos-PLoc: An ensemble classifier for predicting subcellular localization of Gram- ositive bacterial proteins. Protein Engineering, Design, and Selection, 20, 39-46.

[66]   Chou, K.C., Shen, H.B. (2006) Large-scale predictions of Gram-negative bacterial protein subcellular locations. Journal of Proteome Research, 5, 3420-3428.

[67]   Shen, H.B., Chou, K.C. (2007) Virus-PLoc: A fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells. Biopolymers, 85, 233-240.

[68]   Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel- arver, L., Kasarskis, A., Lewis, S., Matese, J.C., Rich-ardson, J.E., Ringwald, M., Rubin, G.M., Sherlock, G. (2000) Gene ontology: Tool for the unification of biology. Nature Genetics, 25, 25-29.

[69]   Chou, K.C. (2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioin-formatics, 21, 10-19.

[70]   Chou, K.C., Shen, H.B. (2010) A new method for pre-dicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0. PLoS ONE, 5, e9931.

[71]   Shen, H.B., Chou, K.C. (2009) A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0. Analytical Biochemistry, 394, 269-74.

[72]   Chou, K.C., Shen, H.B. (2010) Plant-mPLoc: A top- down strategy to augment the power for predicting plant protein subcellular localization. PLoS ONE, 5, e11335.

[73]   Shen, H.B., Chou, K.C. (2009) Gpos-mPLoc: A top- down approach to improve the quality of predicting sub-cellular localization of Gram-positive bacterial proteins. Protein & Peptide Letters, 16, 1478-1484.

[74]   Shen, H.B., Chou, K.C. (2010) Gneg-mPLoc: A top- down strategy to enhance the quality of predicting sub-cellular localization of Gram-negative bacterial proteins. Journal of Theoretical Biology, 264, 326-333.

[75]   Shen, H.B., Chou, K.C. (2010) Virus-mPLoc: A fusion classifier for viral protein subcellular location prediction by incorporating multiple sites. Journal of Biomolecular Structure & Dynamics, 28, 175-186.

[76]   Chou, K.C., Zhang, C.T. (1995) Review: Prediction of protein structural classes. Critical Reviews in Biochemistry and Molecular Biology, 30, 275-349.

[77]   Fang, Y., Guo, Y., Feng, Y., Li, M. (2008) Predicting DNA-binding proteins: Approached from Chou’s pseudo amino acid composition and other specific sequence fea-tures. Amino Acids, 34, 103-109.

[78]   Feng, Y.E., Luo, L.F. (2008) Use of tetrapeptide signals for protein secondary-structure prediction. Amino Acids, 35, 607-614.

[79]   Li, Z.C., Zhou, X.B., Dai, Z., Zou, X.Y. (2009) Prediction of protein structural classes by Chou’s pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis. Amino Acids, 37, 415-425.

[80]   Nanni, L., Lumini, A. (2008) Genetic programming for creating Chou's pseudo amino acid based features for submitochondria localization. Amino Acids, 34, 653-660.

[81]   Wang, Y., Xue, Z., Shen, G., Xu, J. (2008) PRINTR: Prediction of RNA binding sites in proteins using SVM and profiles. Amino Acids, 35, 295-302.

[82]   Zhao, X.M., Chen, L., Aihara, K. (2008) Protein function prediction with high-throughput data. Amino Acids, 35, 517-530.

[83]   Jahandideh, S., Abdolmaleki, P., Jahandideh, M., Asada-badi, E.B. (2007) Novel two-stage hybrid neural discri-minant model for predicting proteins structural classes. Biophys. Chem., 128, 87-93.

[84]   Chen, K., Kurgan, L.A., Ruan, J. (2008) Prediction of protein structural class using novel evolutionary colloca-tion-based sequence representation. Journal of Computa-tional Chemistry, 29, 1596-1604.

[85]   Jahandideh, S., Sarvestani, A.S., Abdolmaleki, P., Jahan-dideh, M., Barfeie, M. (2007) Gamma-Turn types predic-tion in proteins using the support vector machines. Jour-nal of Theoretical Biology, 249, 785-790.

[86]   Shao, X., Tian, Y., Wu, L., Wang, Y., L., J., Deng, N. (2009) Predicting DNA- and RNA-binding proteins from sequences with kernel methods. Journal of Theoretical Biology, 258, 289-293.

[87]   Yang, J.Y., Peng, Z.L., Yu, Z.G., Zhang, R.J., Anh, V., Wang, D. (2009) Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation. Journal of Theoretical Biology, 257, 618-626.

[88]   Anand, A., Suganthan, P.N. (2009) Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates. Journal of Theoretical Biology, 259, 533-540.

[89]   Chen, C., Chen, L.X., Zou, X.Y., Cai, P.X. (2008) Pre-dicting protein structural class based on multi-features fusion. Journal of Theoretical Biology, 253, 388-392.

[90]   Du, P., Li, Y. (2008) Prediction of C-to-U RNA editing sites in plant mitochondria using both biochemical and evolutionary information. Journal of Theoretical Biology, 253, 579-589.

[91]   Jahandideh, S., Hoseini, S., Jahandideh, M., Hoseini, A., Disfani, F.M. (2009) Gamma-turn types prediction in proteins using the two-stage hybrid neural discriminant model. Journal of Theoretical Biology, 259, 517-522.

[92]   Lin, H. (2008) The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou’s pseudo amino acid composition. Journal of Theoretical Biology, 252, 350-356.

[93]   Munteanu, C.B., Gonzalez-Diaz, H., Magalhaes, A.L. (2008) Enzymes/non-enzymes classification model com-plexity based on composition, sequence, 3D and topo-logical indices. Journal of Theoretical Biology, 254, 476- 482.

[94]   Rezaei, M.A., Abdolmaleki, P., Karami, Z., Asadabadi, E.B., Sherafat, M.A., Abrishami-Moghaddam, H., Fadaie, M., Forouzanfar, M. (2008) Prediction of membrane protein types by means of wavelet analysis and cascaded neural networks. Journal of Theoretical Biology, 254, 817-820.

[95]   Vilar, S., Gonzalez-Diaz, H., Santana, L., Uriarte, E. (2009) A network-QSAR model for prediction of genet-ic-component biomarkers in human colorectal cancer. Journal of Theoretical Biology, 261, 449-458.

[96]   Wang, T., Xia, T., Hu, X.M. (2010) Geometry preserving projections algorithm for predicting membrane protein types. Journal of Theoretical Biology, 262, 208-213.

[97]   Chen, Y., Han, K. (2009) BSFINDER: Finding Binding Sites of HCV Proteins Using a Support Vector Machine. Protein & Peptide Letters, 16, 373-382.

[98]   Kannan, S., Hauth, A.M., Burger, G. (2008) Function prediction of hypothetical proteins without sequence si-milarity to proteins of known function. Protein & Peptide Letters, 15, 1107-1116.

[99]   Nanni, L., Lumini, A. (2009) A Further Step Toward an Optimal Ensemble of Classifiers for Peptide Classification, a Case Study: HIV Protease. Protein & Peptide Letters, 16, 163-167.

[100]   Gu, F., Chen, H. (2009) Evaluating Long-term Relation-ship of Protein Sequence by Use of d-Interval Conditional Probability and its Impact on Protein Structural Class Prediction. Protein Pept. Lett., 16, 1267-1276.

[101]   Ji, G., Wu, X., Shen, Y., Huang, J., Quinn Li, Q. (2010) A classification-based prediction model of messenger RNA polyadenylation sites. Journal of Theoretical Biology, 265, 287-296.

[102]   Yang, X.Y., Shi, X.H., Meng, X., Li, X.L., Lin, K., Qian, Z.L., Feng, K.Y., Kong, X.Y., Cai, Y.D. (2010) Classifi-cation of transcription factors using protein primary structure. Protein & Peptide Letters, 17, 899-908.

[103]   Gu, Q., Ding, Y.S., Zhang, T.L. (2010) Prediction of G-Protein-Coupled Receptor Classes in Low Homology Using Chou’s Pseudo Amino Acid Composition with Approximate Entropy and Hydrophobicity Patterns. Pro-tein Pept. Lett., 17, 559-567.

[104]   Liu, L., He, D., Yang, S., Xu, Y. (2010) Applying che-mometrics approaches to model and predict the binding affinities between the human amphiphysin SH3 domain and its peptide ligands. Protein Pept. Lett., 17, 246- 253.

[105]   Shi, R., Hu, X. (2010) Predicting enzyme subclasses by using support vector machine with composite vectors. Protein Pept. Lett., 17, 599-604.

[106]   Wang, T., Yang, J. (2010) Predicting subcellular localiza-tion of gram-negative bacterial proteins by linear dimen-sionality reduction method. Protein Pept. Lett., 17, 32-37.

[107]   Yang, J., Jiang, X.F. (2010) A novel approach to predict protein-protein interactions related to Alzheimer’s disease based on complex network. Protein Pept. Lett., 17, 356-366.

[108]   Chou, K.C., Shen, H.B. (2006) Hum-PLoc: A novel en-semble classifier for predicting human protein subcellular localization. Biochemical and Biophysical Research Communications, 347, 150-157.

[109]   Chou, K.C., Shen, H.B. (2006) Predicting eukaryotic protein subcellular location by fusing optimized evi-dence-theoretic K-nearest neighbor classifiers. Journal of Proteome Research, 5, 1888-1897.

[110]   Chou, K.C., Shen, H.B. (2007) Review: Recent progresses in protein subcellular location prediction. Analytical Biochemistry, 370, 1-16.

[111]   Small, I., Peeters, N., Legeai, F., Lurin, C. (2004) Predotar: A tool for rapidly screening proteomes for N-terminal targeting sequences. Proteomics, 4, 1581-1590.

[112]   Camon, E., Magrane, M., Barrell, D., Binns, D., Fleisch-mann, W., Kersey, P., Mulder, N., Oinn, T., Maslen, J., Cox, A., Apweiler, R. (2003) The gene ontology annotation (GOA) project: Implementation of GO in SWISS- PROT, TrEMBL, and InterPro. Genome Res., 13, 662-672.

[113]   Barrell, D., Dimmer, E., Huntley, R.P., Binns, D., O'Do-novan, C., Apweiler, R. (2009) The GOA database in 2009-an integrated Gene Ontology Annotation resource. Nucleic Acids Research, 37, D396-403.

[114]   Marchler-Bauer, A., Anderson, J.B., Derbyshire, M.K., DeWeese-Scott, C., Gonzales, N.R., Gwadz, M., Hao, L., He, S., Hurwitz, D.I., Jackson, J.D., Ke, Z., Krylov, D., Lanczycki, C.J., Liebert, C.A., Liu, C., Lu, F., Lu, S., Marchler, G.H., Mullokandov, M., Song, J.S., Thanki, N., Yamashita, R.A., Yin, J.J., Zhang, D., Bryant, S.H. (2007) CDD: A conserved domain database for interactive domain family analysis. Nucleic Acids Research, 35, D237- D240.

 
 
Top