Zhejiang Gongshang University

About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.

Sequence Based Prediction of DNA-Binding Proteins Based on Hybrid Feature Selection Using Random Forest and Gaussian Naïve Bayes

Abstract: Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naive Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader) were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that the proposed DBPPred can be an alternative perspective predictor for large-scale determination of DNA-binding proteins.

REPLACE: A Reliable Trust-Based Platoon Service Recommendation Scheme in VANET

Abstract: The fast development of intelligent transportation has paved the way for innovative techniques for highways, and an entirely new driving pattern of highway vehicular platooning might offer a solution to the persistent problem of road congestion, travel comfort, and road safety. In this vehicular platooning system, a platoon head vehicle provides platoon service to its user vehicles. However, some badly behaved platoon head vehicles may put the platoon in danger, which makes it crucial for user vehicles to distinguish and avoid them. In this paper, we propose a reliable trust-based platoon service recommendation scheme, which is called REPLACE, to help the user vehicles avoid choosing badly behaved platoon head vehicles. Specifically, at the core of REPLACE, a reputation system is designed for the platoon head vehicles by collecting and modeling their user vehicle's feedback. Then, an iterative filtering algorithm is designed to deal with the untruthful feedback from user vehicles. A detailed security analysis is given to show that our proposed REPLACE scheme is secure and robust against badmouth, ballot-stuffing, newcomers, and on–off attacks that exist in vehicular ad hoc networks (VANETs). In addition, we conduct extensive experiments to demonstrate the correctness, accuracy, and robustness of our proposed scheme.