Multi-criteria optimization classifier using fuzzification, kernel and penalty factors for predicting protein interaction hot spots

In order to understand the patterns of various biological processes and discover the principles of protein-protein interactions (PPI), it is important to develop effective methods for identifying and predicting PPI and their hot spots accurately. As for multi-criteria optimization classifier (MCOC), it can learn a decision function from different classes of training data and use it to predict the class labels of unknown samples. In many real world applications, owing to noises, outliers, imbalanced class distribution, nonlinearly separable problems, and other uncertainties, the predictive performance of MCOC degenerates rapidly. In this paper, we introduce a fuzzy contribution to each instance of training data, the unequal penalty factors to the samples in imbalanced classes, and kernel method to nonlinearly separable dataset, then a novel multi-criteria optimization classifier with fuzzification, kernel and penalty factors (FKP-MCOC) is constructed so as to reduce the effects of anomalies, improve the class imbalanced performance, and nonlinear separability in classification. The experimental results of predicting active compounds and protein interaction hot spots and comparison with MCOC, support vector machines (SVM) and fuzzy SVM, the conclusion shows that FKP-MCOC significantly increases the efficiency of classification, the partition of active and inactive compounds in bioassay, the separation of hot spot residues and energetically unimportant residues in protein interactions, and the generalization of predicting active compounds and hot spot residues in new instances.