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

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...

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Bibliographic Details
Published in:Molecules (Basel, Switzerland) Switzerland), 2018-08, Vol.23 (8), p.2000
Main Authors: Tan, Jiu-Xin, Dao, Fu-Ying, Lv, Hao, Feng, Peng-Mian, Ding, Hui
Format: Article
Language:English
Subjects:
Algorithms
Analysis of Variance
ANOVA
Bacteriophages
Computational Biology - methods
Databases, Protein
Experimental methods
feature fusion
Identification methods
Learning algorithms
Lysis
machine learning
Methods
mRMR
phage virion protein
Phages
Proteins
Reproducibility of Results
ROC Curve
Support Vector Machine
Support vector machines
Test procedures
Variance analysis
Viral Proteins - chemistry
Virion
Virions
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Summary: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.
ISSN:1420-3049
1420-3049
DOI:10.3390/molecules23082000