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Tsinghua Science and Technology 2015, 20(5): 491-499 https://doi.org/10.1109/TST.2015.7297748
Open Access | Issue | Published: 13 October 2015

A Feature Selection Method for Prediction Essential Protein

Show Author's Information 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.
Keywords:
machine learning, feature selection, essential protein, Protein-Protein Interaction (PPI), centrality algorithm
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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Abstract Full text About this article

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.


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About this article

A Feature Selection Method for Prediction Essential Protein

Show Author's information 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.

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: machine learning, feature selection, essential protein, Protein-Protein Interaction (PPI), centrality algorithm

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Publication history

Received: 21 May 2015
Accepted: 06 August 2015
Published: 13 October 2015
Issue date: October 2015

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The author(s) 2015

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61232001, 61502166, 61502214, 61379108, and 61370024) and Scientific Research Fund of Hunan Provincial Education Department (Nos. 15CY007 and 10A076).

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