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Molecules 2018, 23(8), 2000; https://doi.org/10.3390/molecules23082000

Identifying Phage Virion Proteins by Using Two-Step Feature Selection Methods

1
Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
2
Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, School of Public Health, North China University of Science and Technology, Tangshan 063000, China
*
Authors to whom correspondence should be addressed.
Received: 13 July 2018 / Revised: 30 July 2018 / Accepted: 8 August 2018 / Published: 10 August 2018
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Abstract

Accurate identification of phage virion protein is not only a key step for understanding the function of the phage virion protein but also helpful for further understanding the lysis mechanism of the bacterial cell. Since traditional experimental methods are time-consuming and costly for identifying phage virion proteins, it is extremely urgent to apply machine learning methods to accurately and efficiently identify phage virion proteins. In this work, a support vector machine (SVM) based method was proposed by mixing multiple sets of optimal g-gap dipeptide compositions. The analysis of variance (ANOVA) and the minimal-redundancy-maximal-relevance (mRMR) with an increment feature selection (IFS) were applied to single out the optimal feature set. In the five-fold cross-validation test, the proposed method achieved an overall accuracy of 87.95%. We believe that the proposed method will become an efficient and powerful method for scientists concerning phage virion proteins. View Full-Text
Keywords: phage virion protein; feature fusion; ANOVA; mRMR; machine learning phage virion protein; feature fusion; ANOVA; mRMR; machine learning
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Tan, J.-X.; Dao, F.-Y.; Lv, H.; Feng, P.-M.; Ding, H. Identifying Phage Virion Proteins by Using Two-Step Feature Selection Methods. Molecules 2018, 23, 2000.

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