Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties

Abstract Functional diversity rather than species richness is critical for the understanding of ecological patterns and processes. This study aimed to develop novel integrated analytical strategies for the functional characterization of fish diversity based on the quantification, prediction and inte...

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Autores principales:	Feifei Wei, Kengo Ito, Kenji Sakata, Taiga Asakura, Yasuhiro Date, Jun Kikuchi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/877e9d50a9e24c68a5002c8b0448a1d4
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spelling	oai:doaj.org-article:877e9d50a9e24c68a5002c8b0448a1d42021-12-02T12:14:50ZFish ecotyping based on machine learning and inferred network analysis of chemical and physical properties10.1038/s41598-021-83194-02045-2322https://doaj.org/article/877e9d50a9e24c68a5002c8b0448a1d42021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83194-0https://doaj.org/toc/2045-2322Abstract Functional diversity rather than species richness is critical for the understanding of ecological patterns and processes. This study aimed to develop novel integrated analytical strategies for the functional characterization of fish diversity based on the quantification, prediction and integration of the chemical and physical features in fish muscles. Machine learning models with an improved random forest algorithm applied on 1867 muscle nuclear magnetic resonance spectra belonging to 249 fish species successfully predicted the mobility patterns of fishes into four categories (migratory, territorial, rockfish, and demersal) with accuracies of 90.3–95.4%. Markov blanket-based feature selection method with an ecological–chemical–physical integrated network based on the Bayesian network inference algorithm highlighted the importance of nitrogen metabolism, which is critical for environmental adaptability of fishes in nutrient-rich environments, in the functional characterization of fish biodiversity. Our study provides valuable information and analytical strategies for fish home-range assessment on the basis of the chemical and physical characterization of fish muscle, which can serve as an ecological indicator for fish ecotyping and human impact monitoring.Feifei WeiKengo ItoKenji SakataTaiga AsakuraYasuhiro DateJun KikuchiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Feifei Wei Kengo Ito Kenji Sakata Taiga Asakura Yasuhiro Date Jun Kikuchi Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties
description	Abstract Functional diversity rather than species richness is critical for the understanding of ecological patterns and processes. This study aimed to develop novel integrated analytical strategies for the functional characterization of fish diversity based on the quantification, prediction and integration of the chemical and physical features in fish muscles. Machine learning models with an improved random forest algorithm applied on 1867 muscle nuclear magnetic resonance spectra belonging to 249 fish species successfully predicted the mobility patterns of fishes into four categories (migratory, territorial, rockfish, and demersal) with accuracies of 90.3–95.4%. Markov blanket-based feature selection method with an ecological–chemical–physical integrated network based on the Bayesian network inference algorithm highlighted the importance of nitrogen metabolism, which is critical for environmental adaptability of fishes in nutrient-rich environments, in the functional characterization of fish biodiversity. Our study provides valuable information and analytical strategies for fish home-range assessment on the basis of the chemical and physical characterization of fish muscle, which can serve as an ecological indicator for fish ecotyping and human impact monitoring.
format	article
author	Feifei Wei Kengo Ito Kenji Sakata Taiga Asakura Yasuhiro Date Jun Kikuchi
author_facet	Feifei Wei Kengo Ito Kenji Sakata Taiga Asakura Yasuhiro Date Jun Kikuchi
author_sort	Feifei Wei
title	Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties
title_short	Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties
title_full	Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties
title_fullStr	Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties
title_full_unstemmed	Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties
title_sort	fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/877e9d50a9e24c68a5002c8b0448a1d4
work_keys_str_mv	AT feifeiwei fishecotypingbasedonmachinelearningandinferrednetworkanalysisofchemicalandphysicalproperties AT kengoito fishecotypingbasedonmachinelearningandinferrednetworkanalysisofchemicalandphysicalproperties AT kenjisakata fishecotypingbasedonmachinelearningandinferrednetworkanalysisofchemicalandphysicalproperties AT taigaasakura fishecotypingbasedonmachinelearningandinferrednetworkanalysisofchemicalandphysicalproperties AT yasuhirodate fishecotypingbasedonmachinelearningandinferrednetworkanalysisofchemicalandphysicalproperties AT junkikuchi fishecotypingbasedonmachinelearningandinferrednetworkanalysisofchemicalandphysicalproperties
_version_	1718394584746164224

Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties

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