Document Type
Article
Publication Date
2021
Department
Department of Biological Sciences
Abstract
Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities.
Publication Title
Computational and Structural Biotechnology Journal
Recommended Citation
Ghannam, R.,
&
Techtmann, S.
(2021).
Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring.
Computational and Structural Biotechnology Journal,
19, 1092-1107.
http://doi.org/10.1016/j.csbj.2021.01.028
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/14678
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. Publisher’s version of record: https://doi.org/10.1016/j.csbj.2021.01.028