Applying the Naïve Bayes classifier with kernel density estimation to the prediction of protein–protein interaction sites

Abstract

Freely available on the web at http://tardis.nibio.go.jp/PSIVER/

Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size

“…Specifically, two criteria were calculated; the overall accuracy of the training data that could be thought as an index that express the successful power of the model and the overall accuracy on the validation data that could be thought as an index that express the predictive power of the model. In both cases, the accuracy was calculated as the ration of the true positive plus the true negative to the total number of data (Murakami and Mizuguchi, 2010).…”

Computational Tools and Databases for the Study and Characterization of Protein Interactions

“…Early examples of approaches in this category include a NN (Ofran and Rost 2003) that uses local sequence information, which was subsequently improved by including a post-neural network filtering step . Other approaches include SVMs that combine sequence profiles and other sequence-based information such as spatially neighbouring residues (Koike and Takagi 2004;Res et al 2005;Chen and Li 2010), a RF that integrates physicochemical properties of residues, evolutionary conservation and amino acid distances (Chen and Jeong 2009), and a naive Bayesian classifier trained to integrate position-specific scoring matrix and predicted accessibility (Murakami and Mizuguchi 2010). Finally, other sequence-based methods have been developed to improve prediction by tacking issues such as the problem of unbalanced data in protein sets (Yu et al 2010), i.e.…”

Machine learning approach to an otoneurological classification problem

“…Furthermore, in the case of HAH-SVM we applied the binary SVM and k-NN implementations of Matlab in Bioinformatics Toolbox and Naïve Bayes and Multinomial Logistic Regression implementations in Statistics Toolbox. For Naïve Bayes kernel density estimation [4], [13] was applied. Least Squares method [15] was used in finding optimal hyperplane for SVM.…”