Table 2.

Comparison of online carbonylation predictors used for machine learning methods. Abbreviations: KNN, k-nearest neighbor; ROC, Receiver Operating Characteristic

Carbonylation predictor	Machine learning algorithm	Parameters	Data Set	Area under the ROC1	Reference
CarSPred	Weighted support vector machine	Position-specific propensity of amino acid, k-spaced amino acid pair, KNN scores, physicochemical properties (electric properties, hydrophobicity, alpha and turn propensities, etc.)	331 lysine, 131 arginine, 128 threonine, and 129 proline carbonylation sites were extracted from 230 carbonylated human proteins. In addition, 22 lysine, 3 arginine, 6 threonine, and 15 proline carbonylation sites were extracted from carbonylated mouse, rabbit and bovine proteins.	Lysine: 0.6704 Arginine: 0.5345 Threonine: 0.6800 Proline: 0.7873	106
iCar-PseCp	Random forest	pseudo amino acid composition	Data was derived from 230 human carbonylated protein sequences and 20 carbonylated proteins from Photobacterium and Escherichia coli.	Lysine: 0.8728 Arginine: 0.8668 Threonine: 0.8603 Proline: 0.8484	108
iCarPS	Random forest	3-D conical coordinates and physicochemical properties (hydrophobicity, hydrophilicity, mass, pK1, pK2, pI, rigidity, flexibility, and irreplaceability)	Same benchmark dataset as Lv, et al. (2014).109	Lysine: 0.789 Arginine: 0.726 Threonine: 0.790 Proline: 0.814	109

¹Area under the curve was derived for the ROC. The ROC plots the sensitivity (i.e., true positive rate) versus selectivity.