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Progress in predicting bacteriophage host ranges by intelligent computing
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Boyu WANG1, 2, Zizi YANG1, Fengzhu SUN3, Ying WANG1, 2, 4, *
Acta Microbiologica Sinica | 2024, 64(2) : 344 - 363
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Acta Microbiologica Sinica | 2024, 64(2): 344-363
• Reviews •
Progress in predicting bacteriophage host ranges by intelligent computing
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Boyu WANG1, 2, Zizi YANG1, Fengzhu SUN3, Ying WANG1, 2, 4, *
Affiliations
  • 1 Department of Automation, Xiamen University, Xiamen 361000, Fujian, China
  • 2 National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361000, Fujian, China
  • 3 Department of Quantitative and Computational Biology, University of Southern California, Los Angeles CA90089, California, USA
  • 4 Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision, Xiamen 361000, Fujian, China
Published: 2024-02-04 doi: 10.13343/j.cnki.wsxb.20230418
Outline
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The prediction of bacteriophage host ranges is of great significance for the basic research and clinical application of bacteriophages. The conventional biological experimental methods are limited by the poor culturability of bacteriophages and strict cultivation conditions. The availability of massive genome or metagenome sequencing data provides the sequence signature of bacteriophages and bacteria. Therefore, intelligent computing serves as a feasible way to predict bacteriophage host ranges. This paper systematically reviews the studies about intelligent computing-based prediction of bacteriophage host ranges. Starting from the process of bacteriophage infecting bacteria, we describe the feature source and biological rationality of prediction models, analyze the typical intelligent models and their prediction principles, and list all the reference datasets, real-world datasets, and evaluation indicators. The review aims to improve the understanding on the mechanism of bacteriophages in invading bacterial hosts and promote the usage of bacteriophages as antibiotic substitutes in biological therapy.

microbiome  /  bacteriophage-host interactions  /  prediction model  /  intelligent computing  /  machine learning  /  neural network
Boyu WANG, Zizi YANG, Fengzhu SUN, Ying WANG. Progress in predicting bacteriophage host ranges by intelligent computing[J]. Acta Microbiologica Sinica, 2024 , 64 (2) : 344 -363 . DOI: 10.13343/j.cnki.wsxb.20230418
Funding
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  • National Natural Science Foundation of China(62173282)
  • National Key Research and Development Program of China(2018YFD0901401)
Appendix
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Year 2024 volume 64 Issue 2
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144
66
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Article Info
doi: 10.13343/j.cnki.wsxb.20230418
  • Receive Date：2023-06-15
  • Online Date：2026-03-18
  • Published：2024-02-04
Article Data
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History
  • Received：2023-06-15
  • Accepted：2023-08-17
Funding
National Natural Science Foundation of China(62173282)
National Key Research and Development Program of China(2018YFD0901401)
Affiliations
    1 Department of Automation, Xiamen University, Xiamen 361000, Fujian, China
    2 National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361000, Fujian, China
    3 Department of Quantitative and Computational Biology, University of Southern California, Los Angeles CA90089, California, USA
    4 Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision, Xiamen 361000, Fujian, China

Corresponding:

*WANG Ying, Tel: +86-592-2182338, E-mail:
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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