Identification of Interface Residues Involved in Protein-Protein Interactions Using Naïve Bayes Classifier

Wang, Chishe; Jia-xing, Cheng; Su, Shoubao; Xu, Dongzhe

doi:10.1007/978-3-540-88192-6_20

Cited by 3 publications

(5 citation statements)

References 24 publications

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“…When using a combination of features for profile, ASA, entropy, and weight, we get a better evaluation result, as shown in Table 4. Compared with the literature [4] selected combination of features (profile + ASA), recall of positive class increased nearly by 2%, CC increased nearly by 2%, and AUCC increased nearly by 4%, where the Percentage value is 120%.…”

Section: Multifeature Combinationmentioning

confidence: 62%

“…The literature [4] achieved an accuracy of 68.1%, recall of 40.2%, and precision of 49.9%, where recall and precision were the average values of the positive and negative samples. However, the researchers only concerned the accuracy rate of positive sample during the process of sites prediction in most cases.…”

Section: Resultsmentioning

confidence: 95%

“…However, the researchers only concerned the accuracy rate of positive sample during the process of sites prediction in most cases. The RBFIS we proposed in the paper for the prediction of the interaction sites was compared with the Bayesian method in [4]. The value of recall and -measure in our method was improved greatly shown in Figure 3.…”

Section: Resultsmentioning

confidence: 99%

“…Here we selected the experimental datasets from the paper of Wang et al [4], which had strong representation in the prediction of protein binding sites and also had been used by many other researchers. The datasets included 70 heterologous protein complexes as the training data, selected from eight different kinds of protein complexes.…”

Section: Datasetsmentioning

confidence: 99%

“…Neuvirth et al [3] proposed an algorithm called ProMate which can successfully predict about 70% of the interface residues of the proteins, and the success rate of the method was equal whether applied on the unbound DB or on the not-intersected bound DB. Wang et al [4] use naive Bayes classifier to predict the interface residues of protein-protein interaction for a dataset of representative heterologous protein complexes, achieving accuracy rate of 68%, specificity of 40.2%, and sensitivity of 49.9%. Bradford et al [5] provided a Bayesian network to predict the protein binding sites with an overall success rate of 82% on a dataset of 180 proteins which improved by 36% on a random method.…”

Section: Introductionmentioning

confidence: 99%

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The Prediction of Protein-Protein Interaction Sites Based on RBF Classifier Improved by SMOTE

Wang

2014

Mathematical Problems in Engineering

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Protein-protein interaction sites are the basis of biomolecule interactions, which are widely used in drug target identification and new drug discovery. Traditional site predictors of protein-protein interaction mostly based on unbalanced datasets, the classification results tend to negative class, resulting in a lower predictive accuracy for positive class. A method called RBFIS (radial basis function improved by SMOTE) is presented in the paper to address the problem. The intelligent algorithm SMOTE is used to artificially synthesize the imbalanced datasets of negative sample classes. Simultaneously, KNN algorithm is utilized to interpolate values between the minority class samples to generate new samples, making the sample data tend to balance as much as possible. Then, RBF classifier is used to construct the site predictor of protein-protein interaction based on the processed quasi-equilibrium sample sets. The results of experiments indicated that the method had an improvement on recall and f -measure of positive class compared with traditional methods by 12% and 25%. Moreover, many rounds of experiments were performed for different combinations of features. It was observed that the key combination of different multiple features can better efficiently improve the prediction performance.In conclusion, the studies we have performed show that the proposed method is better for dealing with the imbalanced protein interaction sites.

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Section: Multifeature Combinationmentioning

confidence: 62%

Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%