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- W2885768951 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." @default.
- W2885768951 created "2018-08-22" @default.
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- W2885768951 date "2018-08-10" @default.
- W2885768951 modified "2023-10-18" @default.
- W2885768951 title "Identifying Phage Virion Proteins by Using Two-Step Feature Selection Methods" @default.
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- W2885768951 doi "https://doi.org/10.3390/molecules23082000" @default.
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