Machine learning and deep learning applications in microbiome research

Research output: Contribution to journal › Journal article › Research › peer-review

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Machine learning and deep learning applications in microbiome research. / Hernandez Medina, Ricardo; Kutuzova, Svetlana; Nielsen, Knud Nor; Johansen, Joachim; Hansen, Lars Hestbjerg; Nielsen, Mads; Rasmussen, Simon.

In: ISME Communications, Vol. 2, 98, 2022.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Hernandez Medina, R, Kutuzova, S, Nielsen, KN, Johansen, J, Hansen, LH, Nielsen, M & Rasmussen, S 2022, 'Machine learning and deep learning applications in microbiome research', ISME Communications, vol. 2, 98. https://doi.org/10.1038/s43705-022-00182-9

APA

Hernandez Medina, R., Kutuzova, S., Nielsen, K. N., Johansen, J., Hansen, L. H., Nielsen, M., & Rasmussen, S. (2022). Machine learning and deep learning applications in microbiome research. ISME Communications, 2, [98]. https://doi.org/10.1038/s43705-022-00182-9

Vancouver

Hernandez Medina R, Kutuzova S, Nielsen KN, Johansen J, Hansen LH, Nielsen M et al. Machine learning and deep learning applications in microbiome research. ISME Communications. 2022;2. 98. https://doi.org/10.1038/s43705-022-00182-9

Author

Hernandez Medina, Ricardo ; Kutuzova, Svetlana ; Nielsen, Knud Nor ; Johansen, Joachim ; Hansen, Lars Hestbjerg ; Nielsen, Mads ; Rasmussen, Simon. / Machine learning and deep learning applications in microbiome research. In: ISME Communications. 2022 ; Vol. 2.

Bibtex

@article{69775c54ee414354be1b65886e29b33d,
title = "Machine learning and deep learning applications in microbiome research",
abstract = "The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern and influence macroscopic systems including human health, plant resilience, and biogeochemical cycling. Such feats have attracted interest from the scientific community, which has recently turned to machine learning and deep learning methods to interrogate the microbiome and elucidate the relationships between its composition and function. Here, we provide an overview of how the latest microbiome studies harness the inductive prowess of artificial intelligence methods. We start by highlighting that microbiome data – being compositional, sparse, and high-dimensional – necessitates special treatment. We then introduce traditional and novel methods and discuss their strengths and applications. Finally, we discuss the outlook of machine and deep learning pipelines, focusing on bottlenecks and considerations to address them.",
author = "{Hernandez Medina}, Ricardo and Svetlana Kutuzova and Nielsen, {Knud Nor} and Joachim Johansen and Hansen, {Lars Hestbjerg} and Mads Nielsen and Simon Rasmussen",
year = "2022",
doi = "10.1038/s43705-022-00182-9",
language = "English",
volume = "2",
journal = "ISME Communications",

}

RIS

TY - JOUR

T1 - Machine learning and deep learning applications in microbiome research

AU - Hernandez Medina, Ricardo

AU - Kutuzova, Svetlana

AU - Nielsen, Knud Nor

AU - Johansen, Joachim

AU - Hansen, Lars Hestbjerg

AU - Nielsen, Mads

AU - Rasmussen, Simon

PY - 2022

Y1 - 2022

N2 - The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern and influence macroscopic systems including human health, plant resilience, and biogeochemical cycling. Such feats have attracted interest from the scientific community, which has recently turned to machine learning and deep learning methods to interrogate the microbiome and elucidate the relationships between its composition and function. Here, we provide an overview of how the latest microbiome studies harness the inductive prowess of artificial intelligence methods. We start by highlighting that microbiome data – being compositional, sparse, and high-dimensional – necessitates special treatment. We then introduce traditional and novel methods and discuss their strengths and applications. Finally, we discuss the outlook of machine and deep learning pipelines, focusing on bottlenecks and considerations to address them.

AB - The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern and influence macroscopic systems including human health, plant resilience, and biogeochemical cycling. Such feats have attracted interest from the scientific community, which has recently turned to machine learning and deep learning methods to interrogate the microbiome and elucidate the relationships between its composition and function. Here, we provide an overview of how the latest microbiome studies harness the inductive prowess of artificial intelligence methods. We start by highlighting that microbiome data – being compositional, sparse, and high-dimensional – necessitates special treatment. We then introduce traditional and novel methods and discuss their strengths and applications. Finally, we discuss the outlook of machine and deep learning pipelines, focusing on bottlenecks and considerations to address them.

U2 - 10.1038/s43705-022-00182-9

DO - 10.1038/s43705-022-00182-9

M3 - Journal article

VL - 2

JO - ISME Communications

JF - ISME Communications

M1 - 98

ER -

ID: 323873980