» Articles » PMID: 31057526

Application of Machine Learning in Microbiology

Overview
Journal Front Microbiol
Specialty Microbiology
Date 2019 May 7
PMID 31057526
Citations 60
Authors
Affiliations
Soon will be listed here.
Abstract

Microorganisms are ubiquitous and closely related to people's daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but this method is expensive and time consuming. However, the cultivation method cannot keep a pace with the development of high-throughput sequencing technology. To deal with this problem, machine learning (ML) methods have been widely applied to the field of microbiology. Literature reviews have shown that ML can be used in many aspects of microbiology research, especially classification problems, and for exploring the interaction between microorganisms and the surrounding environment. In this study, we summarize the application of ML in microbiology.

Citing Articles

Clinical Microbiology: where do we stand?.

Vatopoulos A Front Antibiot. 2025; 3():1250632.

PMID: 39816265 PMC: 11731818. DOI: 10.3389/frabi.2024.1250632.


A machine learning-based strategy to elucidate the identification of antibiotic resistance in bacteria.

Parthasarathi K, Gaikwad K, Rajesh S, Rana S, Pandey A, Singh H Front Antibiot. 2025; 3():1405296.

PMID: 39816256 PMC: 11732175. DOI: 10.3389/frabi.2024.1405296.


Extreme learning machine for identifying soil-dwelling microorganisms cultivated on agar media.

Struniawski K, Kozera R, Trzcinski P, Marasek-Ciolakowska A, Sas-Paszt L Sci Rep. 2024; 14(1):31034.

PMID: 39730790 PMC: 11680867. DOI: 10.1038/s41598-024-82174-4.


Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.

Tian T, Zhang X, Zhang F, Huang X, Li M, Quan Z Front Microbiol. 2024; 15:1510139.

PMID: 39624726 PMC: 11610450. DOI: 10.3389/fmicb.2024.1510139.


Machine learning classification of archaea and bacteria identifies novel predictive genomic features.

Bobbo T, Biscarini F, Yaddehige S, Alberghini L, Rigoni D, Bianchi N BMC Genomics. 2024; 25(1):955.

PMID: 39402493 PMC: 11472548. DOI: 10.1186/s12864-024-10832-y.


References
1.
Svetnik V, Liaw A, Tong C, Culberson J, Sheridan R, Feuston B . Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003; 43(6):1947-58. DOI: 10.1021/ci034160g. View

2.
Liu Y, Zeng X, He Z, Zou Q . Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources. IEEE/ACM Trans Comput Biol Bioinform. 2016; 14(4):905-915. DOI: 10.1109/TCBB.2016.2550432. View

3.
Feng P, Zhang J, Tang H, Chen W, Lin H . Predicting the Organelle Location of Noncoding RNAs Using Pseudo Nucleotide Compositions. Interdiscip Sci. 2016; 9(4):540-544. DOI: 10.1007/s12539-016-0193-4. View

4.
Cotter P, Hill C, Ross R . Bacteriocins: developing innate immunity for food. Nat Rev Microbiol. 2005; 3(10):777-88. DOI: 10.1038/nrmicro1273. View

5.
Zeng X, Ding N, Rodriguez-Paton A, Zou Q . Probability-based collaborative filtering model for predicting gene-disease associations. BMC Med Genomics. 2018; 10(Suppl 5):76. PMC: 5751590. DOI: 10.1186/s12920-017-0313-y. View