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Open Access

A Feature Selection Method for Prediction Essential Protein

Jiancheng Zhong, Jianxin Wang( ), Wei Peng, Zhen Zhang, Min Li
School of Information Science and Engineering, Central South University, Changsha 410083, China.
College of Polytechnic, Hunan Normal University, Changsha 410083, China.
Computer Center, Kunming University of Science and Technology, Kunming 650093, China.
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Abstract

Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed to identify essential proteins by using these features. However, it is still a big challenge to design an effective method that is able to select suitable features and integrate them to predict essential proteins. In this work, we first collect 26 features, and use SVM-RFE to select some of them to create a feature space for predicting essential proteins, and then remove the features that share the biological meaning with other features in the feature space according to their Pearson Correlation Coefficients (PCC). The experiments are carried out on S. cerevisiae data. Six features are determined as the best subset of features. To assess the prediction performance of our method, we further compare it with some machine learning methods, such as SVM, Naive Bayes, Bayes Network, and NBTree when inputting the different number of features. The results show that those methods using the 6 features outperform that using other features, which confirms the effectiveness of our feature selection method for essential protein prediction.

Keywords

essential proteinfeature selectionProtein-Protein Interaction (PPI)machine learningcentrality algorithm

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Tsinghua Science and Technology
Volume 20 Issue 5,
October 2015
Pages 491-499
DOI: 10.1109/TST.2015.7297748
Cite this article:
Zhong J, Wang J, Peng W, et al. A Feature Selection Method for Prediction Essential Protein. Tsinghua Science and Technology, 2015, 20(5): 491-499. https://doi.org/10.1109/TST.2015.7297748

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Received: 21 May 2015
Accepted: 06 August 2015
Published: 13 October 2015
The author(s) 2015
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