[1]
Davies DR, Cohen GH. Interactions of protein antigens with antibodies. Proc Natl Acad Sci USA 1996; 93(1): 7-12.
[2]
Langeveld JP, Martinez-Torrecuadrada J, Boshuizen RS, Meloen RH, Ignacio Casal J. Characterisation of a protective linear B cell epitope against feline parvoviruses. Vaccine 2001; 19(17-19): 2352-60.
[3]
Barlow DJ, Edwards MS, Thornton JM. Continuous and discontinuous protein antigenic determinants. Nature 1986; 322(6081): 747-8.
[4]
Walter G. Production and use of antibodies against synthetic peptides. J Immunol Methods 1986; 88(2): 149-61.
[5]
Yadav M, Liebau E, Haldar C, Rathaur S. Identification of major antigenic peptide of filarial glutathione-S-transferase. Vaccine 2011; 29(6): 1297-303.
[6]
Schlessinger A, Ofran Y, Yachdav G, Rost B. Epitome: database of structure-inferred antigenic epitopes. Nucleic Acids Res 2006; 34(Database issue): D777-80.
[7]
AntiJen. a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data. Immunome Res 2005; 1(1): 1-12.
[8]
Vita R, Zarebski L, Greenbaum JA, et al. The immune epitope database 2.0. Nucleic Acids Res 2010; 38(Database issue): D854-62.
[9]
Ivanciuc O, Schein CH, Braun W. SDAP: database and computational tools for allergenic proteins. Nucleic Acids Res 2003; 31(1): 359-62.
[10]
Xiao X, Shao S, Ding Y, Huang Z, Chou KC. Using cellular automata images and pseudo amino acid composition to predict protein subcellular location. Amino Acids 2006; 30(1): 49-54.
[11]
Xiao X, Wang P, Chou KC. GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes. J Comput Chem 2009; 30(9): 1414-23.
[12]
Gautam A, Chaudhary K, Kumar R, et al. In silico approaches for designing highly effective cell penetrating peptides. J Transl Med 2013; 11(1): 74.
[13]
Shen W, Cao Y, Cha L, et al. Predicting linear B-cell epitopes using amino acid anchoring pair composition. BioData Min 2015; 8(1): 14.
[14]
Lin SY, Cheng CW, Su EC. Prediction of B-cell epitopes using evolutionary information and propensity scales. BMC Bioinformatics 2013; 14(Suppl. 2): S10.
[15]
Chen J, Liu H, Yang J, Chou KC. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids 2007; 33(3): 423-8.
[16]
Leslie C, Eskin E, Noble WS. The spectrum kernel: a string kernel for SVM protein classification. Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing 2002.
[17]
Zaki NM, Deris S, Illias R. Application of string kernels in protein sequence classification. Appl Bioinformatics 2005; 4(1): 45-52.
[18]
Leslie CS, Eskin E, Cohen A, Weston J, Noble WS. Mismatch string kernels for discriminative protein classification. Bioinformatics 2004; 20(4): 467-76.
[19]
Saigo H, Vert JP, Ueda N, Akutsu T. Protein homology detection using string alignment kernels. Bioinformatics 2004; 20(11): 1682-9.
[20]
Lodhi H, et al. Text classification using string kernels. J Mach Learn Res 2002; 2(3): 419-44.
[21]
Yao B, Zhang L, Liang S, Zhang C. SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity. PLoS One 2012; 7(9): e45152.
[22]
Pruitt KD, Tatusova T, Klimke W, Maglott DR. NCBI Reference Sequences: current status, policy and new initiatives. Nucleic Acids Res 2009; 37: D32-6.
[23]
Ren Y, Chen X, Feng M, Wang Q, Zhou P. Gaussian process: a promising approach for the modeling and prediction of Peptide binding affinity to MHC proteins. Protein Pept Lett 2011; 18(7): 670-8.
[24]
Huang JH, Wen M, Tang LJ, et al. Using random forest to classify linear B-cell epitopes based on amino acid properties and molecular features. Biochimie 2014; 103(1): 1-6.
[25]
Shao J, Xu D, Tsai SN, Wang Y, Ngai SM. Computational identification of protein methylation sites through bi-profile Bayes feature extraction. PLoS One 2009; 4(3): e4920.
[26]
Zheng W, Zhang C, Hanlon M, Ruan J, Gao J. An ensemble method for prediction of conformational B-cell epitopes from antigen sequences. Comput Biol Chem 2014; 49(49C): 51-8.
[27]
Gao J, Faraggi E, Zhou Y, Ruan J, Kurgan L. BEST: improved prediction of B-cell epitopes from antigen sequences. PLoS One 2012; 7(6): e40104.
[28]
Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999; 292(2): 195-202.
[29]
Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT. Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J Mol Biol 2004; 337(3): 635-45.
[30]
Zhang W, Xiong Y, Zhao M, et al. Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature. BMC Bioinformatics 2011; 12(1): 341-12.
[31]
Hu J. Prediction of Discontinuous B-Cell Epitopes Using Logistic Regression and Structural Information. J Proteomics Bioinform 2011; 04(1): 10-5.
[32]
Sun J, et al. Does difference exist between epitope and non-epitope residues? Analysis of the physicochemical and structural properties on conformational epitopes from B-cell. Immunome Res 2011; 7(3): 1-11.
[33]
Chen K, Mizianty MJ, Kurgan L. Prediction and analysis of nucleotide-binding residues using sequence and sequence-derived structural descriptors. Bioinformatics 2012; 28(3): 331-41.
[34]
El-Manzalawy Y, Dobbs D, Honavar V. Predicting flexible length linear B-cell epitopes 2008. 121-32.
[35]
Saha SGPS. Raghava. BcePred: Prediction of Continuous B-Cell Epitopes in Antigenic Sequences Using Physico-chemical Properties. Lect Notes Comput Sci 2004; 3239: 197-204.
[36]
Lian Y, Ge M, Pan XM. EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression. BMC Bioinformatics 2014; 15(1): 414.
[37]
Larsen JE, Lund O, Nielsen M. Improved method for predicting linear B-cell epitopes. Immunome Res 2006; 2(1): 2.
[38]
Liao Z, et al. In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches. BioMed Res Int 2016; 2016: 2375268.
Download PDF Flyer