ArticleJournal of biosciences2015
Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.
Article in Journal of biosciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- MED-PPIS: Multi-order Moments External Graph Attention Network with Dual-Axis Attention for Protein-Protein Interaction Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction.PLoS computational biology · 2023Article
- Article
- Deep Learning for Protein-Protein Interaction Site Prediction.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm.International journal of molecular sciences · 2020Article
- Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets.International journal of molecular sciences · 2020Article
- Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique.Bioinformatics (Oxford, England) · 2019Article
- Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction.BMC bioinformatics · 2018Article
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3 authors.
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Abstract
Protein-protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.
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