Identification of Phage Viral Proteins With Hybrid Sequence Features
The uniqueness of bacteriophages plays an important role in bioinformatics research. In real applications, the function of the bacteriophage virion proteins is the main area of interest. Therefore, it is very important to classify bacteriophage virion proteins and non-phage virion proteins accuratel...
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doaj-4786b700c0334e189eccc38cf1ab0d1c2020-11-24T21:32:42ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2019-03-011010.3389/fmicb.2019.00507445150Identification of Phage Viral Proteins With Hybrid Sequence FeaturesXiaoqing Ru0Lihong Li1Chunyu Wang2School of Information and Electrical Engineering, Hebei University of Engineering, Handan, ChinaSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, ChinaThe uniqueness of bacteriophages plays an important role in bioinformatics research. In real applications, the function of the bacteriophage virion proteins is the main area of interest. Therefore, it is very important to classify bacteriophage virion proteins and non-phage virion proteins accurately. Extracting comprehensive and effective sequence features from proteins plays a vital role in protein classification. In order to more fully represent protein information, this paper is more comprehensive and effective by combining the features extracted by the feature information representation algorithm based on sequence information (CCPA) and the feature representation algorithm based on sequence and structure information. After extracting features, the Max-Relevance-Max-Distance (MRMD) algorithm is used to select the optimal feature set with the strongest correlation between class labels and low redundancy between features. Given the randomness of the samples selected by the random forest classification algorithm and the randomness features for producing each node variable, a random forest method is employed to perform 10-fold cross-validation on the bacteriophage protein classification. The accuracy of this model is as high as 93.5% in the classification of phage proteins in this study. This study also found that, among the eight physicochemical properties considered, the charge property has the greatest impact on the classification of bacteriophage proteins These results indicate that the model discussed in this paper is an important tool in bacteriophage protein research.https://www.frontiersin.org/article/10.3389/fmicb.2019.00507/fullphage virion proteinsmachine learningfeature extractionfeature selectionhybrid sequence features |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaoqing Ru Lihong Li Chunyu Wang |
spellingShingle |
Xiaoqing Ru Lihong Li Chunyu Wang Identification of Phage Viral Proteins With Hybrid Sequence Features Frontiers in Microbiology phage virion proteins machine learning feature extraction feature selection hybrid sequence features |
author_facet |
Xiaoqing Ru Lihong Li Chunyu Wang |
author_sort |
Xiaoqing Ru |
title |
Identification of Phage Viral Proteins With Hybrid Sequence Features |
title_short |
Identification of Phage Viral Proteins With Hybrid Sequence Features |
title_full |
Identification of Phage Viral Proteins With Hybrid Sequence Features |
title_fullStr |
Identification of Phage Viral Proteins With Hybrid Sequence Features |
title_full_unstemmed |
Identification of Phage Viral Proteins With Hybrid Sequence Features |
title_sort |
identification of phage viral proteins with hybrid sequence features |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Microbiology |
issn |
1664-302X |
publishDate |
2019-03-01 |
description |
The uniqueness of bacteriophages plays an important role in bioinformatics research. In real applications, the function of the bacteriophage virion proteins is the main area of interest. Therefore, it is very important to classify bacteriophage virion proteins and non-phage virion proteins accurately. Extracting comprehensive and effective sequence features from proteins plays a vital role in protein classification. In order to more fully represent protein information, this paper is more comprehensive and effective by combining the features extracted by the feature information representation algorithm based on sequence information (CCPA) and the feature representation algorithm based on sequence and structure information. After extracting features, the Max-Relevance-Max-Distance (MRMD) algorithm is used to select the optimal feature set with the strongest correlation between class labels and low redundancy between features. Given the randomness of the samples selected by the random forest classification algorithm and the randomness features for producing each node variable, a random forest method is employed to perform 10-fold cross-validation on the bacteriophage protein classification. The accuracy of this model is as high as 93.5% in the classification of phage proteins in this study. This study also found that, among the eight physicochemical properties considered, the charge property has the greatest impact on the classification of bacteriophage proteins These results indicate that the model discussed in this paper is an important tool in bacteriophage protein research. |
topic |
phage virion proteins machine learning feature extraction feature selection hybrid sequence features |
url |
https://www.frontiersin.org/article/10.3389/fmicb.2019.00507/full |
work_keys_str_mv |
AT xiaoqingru identificationofphageviralproteinswithhybridsequencefeatures AT lihongli identificationofphageviralproteinswithhybridsequencefeatures AT chunyuwang identificationofphageviralproteinswithhybridsequencefeatures |
_version_ |
1725956478855020544 |