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

2010 ◽  
Vol 26 (15) ◽  
pp. 1841-1848 ◽  
Author(s):  
Yoichi Murakami ◽  
Kenji Mizuguchi
Keyword(s):  
Density Estimation ◽  
Protein Interaction ◽  
Kernel Density Estimation ◽  
Naive Bayes ◽  
Kernel Density ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Protein Interaction Sites

Modified naive Bayes classifier for classification of protein-protein interaction sites

2020 ◽  
Vol 26 (02) ◽  
pp. 2177-2184
Author(s):  
M. A. Uddin ◽  
M. S. Ahmed
Keyword(s):  
Protein Interaction ◽  
Protein Sequence ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Protein Interaction Sites

The prediction of protein-protein interaction sites (PPIs) is a vital importance in biology for understanding the physical and functional interactions between molecules in living systems. There are several classification approaches for the prediction of PPI sites; the naïve Bayes classifier is one of the most popular candidates. But the ordinary naïve Bayes classifier is sensitive to unusual protein sequence profiling feature dataset and sometimes it gives ambiguous prediction results. To overcome this problem we have been modified the naïve Bayes classifier by radial basis function (RBF) kernel for the prediction of PPI sites. We investigate the performance of our proposed method compared with the popular classifiers like linear discriminant analysis (LDA), naïve Bayes classifier (NBC), support vector machine (SVM), AdaBoost and k-nearest neighbor (KNN) by the protein sequence profiling data analysis. The mNBC method showed sensitivity (86%), specificity (81%), accuracy (83%) and MCC (65%) for prediction of PPI sites.

scholarly journals Prediction of Protein-Protein Interaction Sites Based on Naive Bayes Classifier

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Haijiang Geng ◽  
Tao Lu ◽  
Xiao Lin ◽  
Yu Liu ◽  
Fangrong Yan
Keyword(s):  
Protein Interaction ◽  
Protein Sequence ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Sequence Features ◽  
Interaction Sites ◽  
Interface Residues

Protein functions through interactions with other proteins and biomolecules and these interactions occur on the so-called interface residues of the protein sequences. Identifying interface residues makes us better understand the biological mechanism of protein interaction. Meanwhile, information about the interface residues contributes to the understanding of metabolic, signal transduction networks and indicates directions in drug designing. In recent years, researchers have focused on developing new computational methods for predicting protein interface residues. Here we creatively used a 181-dimension protein sequence feature vector as input to the Naive Bayes Classifier- (NBC-) based method to predict interaction sites in protein-protein complexes interaction. The prediction of interaction sites in protein interactions is regarded as an amino acid residue binary classification problem by applying NBC with protein sequence features. Independent test results suggested that Naive Bayes Classifier-based method with the protein sequence features as input vectors performed well.

scholarly journals A Comparative Performance Study of Classification Models for Opinion Mining

2022 ◽  
pp. 159-165
Author(s):  
S Raja Rajeswari ◽  
Dr. A. John Sanjeev Kumar
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Kernel Density ◽  
Naïve Bayes ◽  
Support Vector ◽  
Large Set ◽  
Performance Study ◽  
Comparative Performance

Opinion mining has become a major part in today's economy. People would want to know more about a product and the customers opinion before buying it. Companies would also want to know the opinions of the customers. Therefore, analyzing the customer’s opinion is important. A new customer would consider a product as good by analyzing the opinions of other customers. The opinions are collected from various areas, which include blogs, web forums, and product review sites. Classifying these large set of opinions requires a good classifier. In view of this, a comparative study of three classification techniques - Naive Bayes classifier with Kernel Density Estimation (KDE), Support Vector Machine (SVM), Decision Tree and KNN was made. To evaluate the classifier accuracy, precision, recall and F-measure techniques are used. Experimental results show that the Naive Bayes with Kernel Density Estimation (KDE) classifier achieved higher accuracy among others.

Sign in / Sign up

Export Citation Format

Share Document