Computational identification of ubiquitylation sites from protein sequences [11]
Authors used svm, knn, and naive Bayes for analysis and obtained 84.44% accuracy
(2)
2010
Identification, analysis, and prediction of protein ubiquitination sites [12]
Authors used random forest predictor as classification model and obtained 72% accuracy
(3)
2011
Prediction of ubiquitination sites by using the composition of -spaced amino acid pairs [13]
Authors used SVM as classification model and obtained accuracy of 73.40%
(4)
2013
hCKSAAP_UbSite: improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties [14]
Authors used SVM as classification model based on the composition of -spaced amino acid pairs and obtained accuracy of 75.7%
(5)
2014
RUBI: rapid proteomic-scale prediction of lysine ubiquitination and factors influencing predictor performance [15]
Authors proposed Rapid UBIquitination (RUBI), a sequence-based ubiquitination predictor, and obtained 86.8% accuracy
(6)
2014
Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor [16]
Authors proposed RA-T prediction model and obtained 44% improvement across multiple machine learning algorithm
(7)
2016
Prediction of ubiquitination sites with feature weighting scheme and naive Bayes vectorizer [17]
Category based feature weighting scheme is used and prediction model. Proposed technique performed better than SVM
(8)
2016
ESA-UbiSite: accurate prediction of human ubiquitination sites by identifying a set of effective negatives [18]
Authors used evolutionary screening algorithm and obtained testing accuracy 92% and Matthews’ correlation 0.48
(9)
2016
Noncanonical pathway network modelling and ubiquitination site prediction through homology modelling of NF-κB [19]
Authors used loop_model and asses_dope functions and enhanced understanding of cofactors involved and ubiquitination sites employed during the activation process
(10)
2016
Computational methods for ubiquitination site prediction using physicochemical properties of protein sequences [20]
Authors used various techniques like SVM and naive Bayes for predictionm and obtained AUC value greater than or equal to 0.6
(11)
2017
A new scheme to characterize and identify protein ubiquitination sites [21]
Authors used SVM as prediction model and obtained 68.70% average accuracy
