Application of machine learning in bacteriophage research

BMC Microbiology

Yousef Nami¹  Bahman Panahi²  Nazila Imeni³ 

[1] Department of Food Biotechnology, Branch for Northwest & West Region, Agricultural Biotechnology Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Tabriz, Iran;Department of Genomics, Branch for Northwest & West Region, Agricultural Biotechnology Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Tabriz, Iran;Young Researchers and Elite Clube, Marand Branch, Islamic Azad University, Marand, Iran;

关键词: Machine learning;    Bacteriophage;    Classification;    Host;    Life cycle;   

DOI  :  10.1186/s12866-021-02256-5

来源: Springer

【 摘 要 】

Phages are one of the key components in the structure, dynamics, and interactions of microbial communities in different bins. It has a clear impact on human health and the food industry. Bacteriophage characterization using in vitro approaches are time/cost consuming and laborious tasks. On the other hand, with the advent of new high-throughput sequencing technology, the development of a powerful computational framework to characterize the newly identified bacteriophages is inevitable for future research. Machine learning includes powerful techniques that enable the analysis of complex datasets for knowledge discovery and pattern recognition. In this study, we have conducted a comprehensive review of machine learning methods application using different types of features were applied in various aspects of bacteriophage research including, automated curation, identification, classification, host species recognition, virion protein identification, and life cycle prediction. Moreover, potential limitations and advantages of the developed frameworks were discussed.

【 授权许可】

CC BY