Tutturen AEV. Enrichment and identification of citrullinated proteins in biological samples 2014.
Zhang Y, Xie R, Wang J, et al. Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework. Brief Bioinform 2019; 20(6): 2185-99.
Jia C, Zuo Y. Computational prediction of protein O-GlcNAc modification computational. Methods Mol Biol 2018; 1754: 235-46.
Jeatrakul P, Wong KW, Fung CC, Takama Y. IEEE Misclassification analysis for the class imbalance problem. World Automation Congress. Kobe, Japan. 2010.
Hasan MA, Li J, Ahmad S, Molla MK. predCar-site: Carbonylation sites prediction in proteins using support vector machine with resolving data imbalanced issue. Anal Biochem 2017; 525: 107-13.
Veropoulos K, Campbell C, Cristianini N. Controlling the sensitivity of support vector machines. International Joint Conference on AI 1999.
Chou KC. A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. J Biol Chem 1993; 268(23): 16938-48.
Rahman J, Mondal MNI, Islam MKB, Hasan MAM, Amin SMS. Gram-positive bacterial protein subcellular localization prediction using features fusion strategy. 9th International Conference on Electrical and Computer Engineering (ICECE) 2016; 20-2. Dec; 2016.
Xu Y, Shao XJ, Wu LY, Deng NY, Chou KC. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ 2013; 1 e171
Scholkopf B, Smola AJ. Learning with Kernels: Support Vector Machines. Regularization, Optimization, and Beyond 2001.
Vapnik V. Statistical Learning Theory. John Wiley & Sons Inc. New York 1998.
Tatjewski M, Kierczak M, Plewczynski D. Predicting post-translational modifications from local sequence fragments using machine learning algorithms: Overview and best practices. Methods Mol Biol 2017; 1484: 275-300.
Chen Z, Liu X, Li F, et al. Large-scale comparative assessment of computational predictors for lysine post-translational modification sites. Brief Bioinform 2019; 20(6): 2267-90.