Abstract
In the field of bioinformatics, the prediction of phage virion proteins helps us understand the interaction between phage and its host cells and promotes the development of new antibacterial drugs. However, traditional experimental methods to identify phage virion proteins are expensive and inefficient, more researchers are working to develop new computational methods. In this review, we summarized the machine learning methods for predicting phage virion proteins during recent years, and briefly described their advantages and limitations. Finally, some research directions related to phage virion proteins are listed.
Keywords: Bioinformatics, phage virion proteins, machine learning methods, feature selection, protein correlation prediction, cell.
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Current Bioinformatics
Title:Recent Advances on the Machine Learning Methods in Identifying Phage Virion Proteins
Volume: 15 Issue: 7
Author(s): Yingjuan Yang, Chunlong Fan and Qi Zhao*
Affiliation:
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051,China
Keywords: Bioinformatics, phage virion proteins, machine learning methods, feature selection, protein correlation prediction, cell.
Abstract: In the field of bioinformatics, the prediction of phage virion proteins helps us understand the interaction between phage and its host cells and promotes the development of new antibacterial drugs. However, traditional experimental methods to identify phage virion proteins are expensive and inefficient, more researchers are working to develop new computational methods. In this review, we summarized the machine learning methods for predicting phage virion proteins during recent years, and briefly described their advantages and limitations. Finally, some research directions related to phage virion proteins are listed.
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Cite this article as:
Yang Yingjuan , Fan Chunlong and Zhao Qi *, Recent Advances on the Machine Learning Methods in Identifying Phage Virion Proteins, Current Bioinformatics 2020; 15 (7) . https://dx.doi.org/10.2174/1574893614666191203155511
DOI https://dx.doi.org/10.2174/1574893614666191203155511 |
Print ISSN 1574-8936 |
Publisher Name Bentham Science Publisher |
Online ISSN 2212-392X |
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