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Molecules 2018, 23(8), 2000;

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

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
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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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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