### Literature Review  
Several representative tools for PTM glycosylation/glycation site prediction are briefly discussed here. Firstly, glycosylation site prediction methods are summarized. GPP [11] uses the random forest (RF) machine learning method for predicting N-linked and O-linked glycosylation sites based on pairwise sequence patterns and structural features and protein-peptide sequences. Both GlycoPP [12] and GlycoEP [7] employ support vector machine learning method (SVM) for predicting N-linked and O-linked glycosylation sites in prokaryotes and Eukaryotic, respectively, based on amino acid composition (AAC), Binary encoding, position-specific scoring matrix (PSSM), and structural features. GlycoMine [13] and GlycoMinestruct [14] are tools to predict N-linked and O-linked glycosylation sites using the random forest (RF) technique. GlycoMine utilizes functional and sequence-based properties for feature representation while the GlycoMinestruct uses both sequence and structural features. SPRINT-Gly [15] identifies N-linked and O-linked glycosylation sites using deep neural networks and SVM that are trained on huge datasets gathered from six human and mouse databases and based on various sequence, profile, and structural-based features. N-GlyDE [16] is a two-stage N-linked glycosylation site prediction method that employs similarity voting as well as the SVM technique based on gapped dipeptide, Structural-based features. GlycoMine_PU [10] predicts N-linked, O-linked, and C-linked glycosylation sites using a positive unlabeled (PU) learning algorithm based on a variety of sequence, profile and structural-based features. N-GlycoGo [17] predicts N-linked glycosylation sites using XGBoost, an ensemble machine learning model based on eleven feature extraction techniques. PUStackNGly [18] uses PU and stacking ensemble-based learning to predict N-linked glycosylation sites based on various sequence and structure-based features.  
Regarding glycation site prediction, NetGlycate [19] is a sequence-based predictor for glycation site prediction based on artificial neural networks (ANNs). PreGly [20] uses SVM for the prediction of glycation sites based on AAC, amino acid Factor and composition k-spaced amino acid pairs (CKSAAP) features and using maximum relevance minimum redundancy (mRMR) and the incremental feature selection (IFS) methods for feature selection. [PERSON] et al. developed Gly-PseAAC [21], a glycation site prediction model based on sequence order information and position-specific amino acid propensity features using the SVM classifier. BPB_GlySite [1] uses SVM to develop a glycation site prediction model based on bi-profile Bayes feature extraction. [PERSON] et al. developed Glypre [22] model using SVM and based on position scoring function, AAindex, CKSAAP, and structure-based features. [PERSON] et al. proposed iProtGly-SS [5] to predict glycation sites using the SVM method and based on sequence and structure-based features. [PERSON] et al. [9] developed GlyStruct, a glycation site predictor, using SVM machine learning and based on structural information of the amino acid residues. PredGly [8] employs XGBoost and SVM technique to predict glycation site based on combining physicochemical properties, evolutionary-derived and sequence-based features. [PERSON] et al. [6] proposed DeepGly model for glycation site prediction using a recurrent neural network (RNN) and a convolutional neural network (CNN). [PERSON] et al. [23] proposed BERT-Kgly using CNN deep learning to predict glycation site prediction and based on feature extracted by the Bidirectional Encoder Representations from Transformer (BERT). Our previous reviews [24; 25] discuss the previous studies of PTM glycosylation/glycation site prediction in more details.