Prediction of Health of Corals Mussismilia hispida Based on the Microorganisms Present in their Microbiome

Barry Malick Barque; Pedro João Soares Rodrigues; Pedro Luiz de Paula Filho; Raquel Silva Peixoto; Deborah Catharine de Assis Leite

doi:10.1007/978-3-031-53025-8_28

Prediction of Health of Corals Mussismilia hispida Based on the Microorganisms Present in their Microbiome

Barry Malick Barque, Pedro João Soares Rodrigues, Pedro Luiz de Paula Filho, Raquel Silva Peixoto, Deborah Catharine de Assis Leite^*

^*Corresponding author for this work

Marine Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

One of the most diverse and productive marine ecosystems in the world are the corals, providing not only tourism but also an important economic contribution to the countries that have them on their coasts. Thanks to genome sequencing techniques, it is possible to identify the microorganisms that form the coral microbiome. The generation of large amounts of data, thanks to the low cost of sequencing since 2005, provides an opening for the use of artificial neural networks for the advancement of sciences such as biology and medicine. This work aims to predict the healthy microbiome present in samples of Mussismilia hispida coral, using machine learning algorithms, in which the algorithms SVM, Decision Tree, and Random Forest achieved a rate of 61%, 74%, and 72%, respectively. Additionally, it aims to identify possible microorganisms related to the disease in question in corals.

Original language	English (US)
Title of host publication	Optimization, Learning Algorithms and Applications - 3rd International Conference, OL2A 2023, Revised Selected Papers
Editors	Ana I. Pereira, Florbela P. Fernandes, Joao P. Coelho, Armando Mendes, Maria F. Pacheco, Jose Lima
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	409-423
Number of pages	15
ISBN (Print)	9783031530241
DOIs	https://doi.org/10.1007/978-3-031-53025-8_28
State	Published - 2024
Event	3rd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2023 - Ponta Delgada, Portugal Duration: Sep 27 2023 → Sep 29 2023

Publication series

Name	Communications in Computer and Information Science
Volume	1981 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	3rd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2023
Country/Territory	Portugal
City	Ponta Delgada
Period	09/27/23 → 09/29/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Coral reef
Machine learning algorithm
Microbiome

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-031-53025-8_28

Cite this

Barque, B. M., Rodrigues, P. J. S., de Paula Filho, P. L., Peixoto, R. S., & de Assis Leite, D. C. (2024). Prediction of Health of Corals Mussismilia hispida Based on the Microorganisms Present in their Microbiome. In A. I. Pereira, F. P. Fernandes, J. P. Coelho, A. Mendes, M. F. Pacheco, & J. Lima (Eds.), Optimization, Learning Algorithms and Applications - 3rd International Conference, OL2A 2023, Revised Selected Papers (pp. 409-423). (Communications in Computer and Information Science; Vol. 1981 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-53025-8_28

Barque, Barry Malick ; Rodrigues, Pedro João Soares ; de Paula Filho, Pedro Luiz et al. / Prediction of Health of Corals Mussismilia hispida Based on the Microorganisms Present in their Microbiome. Optimization, Learning Algorithms and Applications - 3rd International Conference, OL2A 2023, Revised Selected Papers. editor / Ana I. Pereira ; Florbela P. Fernandes ; Joao P. Coelho ; Armando Mendes ; Maria F. Pacheco ; Jose Lima. Springer Science and Business Media Deutschland GmbH, 2024. pp. 409-423 (Communications in Computer and Information Science).

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abstract = "One of the most diverse and productive marine ecosystems in the world are the corals, providing not only tourism but also an important economic contribution to the countries that have them on their coasts. Thanks to genome sequencing techniques, it is possible to identify the microorganisms that form the coral microbiome. The generation of large amounts of data, thanks to the low cost of sequencing since 2005, provides an opening for the use of artificial neural networks for the advancement of sciences such as biology and medicine. This work aims to predict the healthy microbiome present in samples of Mussismilia hispida coral, using machine learning algorithms, in which the algorithms SVM, Decision Tree, and Random Forest achieved a rate of 61\%, 74\%, and 72\%, respectively. Additionally, it aims to identify possible microorganisms related to the disease in question in corals.",

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Barque, BM, Rodrigues, PJS, de Paula Filho, PL, Peixoto, RS & de Assis Leite, DC 2024, Prediction of Health of Corals Mussismilia hispida Based on the Microorganisms Present in their Microbiome. in AI Pereira, FP Fernandes, JP Coelho, A Mendes, MF Pacheco & J Lima (eds), Optimization, Learning Algorithms and Applications - 3rd International Conference, OL2A 2023, Revised Selected Papers. Communications in Computer and Information Science, vol. 1981 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 409-423, 3rd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2023, Ponta Delgada, Portugal, 09/27/23. https://doi.org/10.1007/978-3-031-53025-8_28

Prediction of Health of Corals Mussismilia hispida Based on the Microorganisms Present in their Microbiome. / Barque, Barry Malick; Rodrigues, Pedro João Soares; de Paula Filho, Pedro Luiz et al.
Optimization, Learning Algorithms and Applications - 3rd International Conference, OL2A 2023, Revised Selected Papers. ed. / Ana I. Pereira; Florbela P. Fernandes; Joao P. Coelho; Armando Mendes; Maria F. Pacheco; Jose Lima. Springer Science and Business Media Deutschland GmbH, 2024. p. 409-423 (Communications in Computer and Information Science; Vol. 1981 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Prediction of Health of Corals Mussismilia hispida Based on the Microorganisms Present in their Microbiome

AU - Barque, Barry Malick

AU - Rodrigues, Pedro João Soares

AU - de Paula Filho, Pedro Luiz

AU - Peixoto, Raquel Silva

AU - de Assis Leite, Deborah Catharine

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - One of the most diverse and productive marine ecosystems in the world are the corals, providing not only tourism but also an important economic contribution to the countries that have them on their coasts. Thanks to genome sequencing techniques, it is possible to identify the microorganisms that form the coral microbiome. The generation of large amounts of data, thanks to the low cost of sequencing since 2005, provides an opening for the use of artificial neural networks for the advancement of sciences such as biology and medicine. This work aims to predict the healthy microbiome present in samples of Mussismilia hispida coral, using machine learning algorithms, in which the algorithms SVM, Decision Tree, and Random Forest achieved a rate of 61%, 74%, and 72%, respectively. Additionally, it aims to identify possible microorganisms related to the disease in question in corals.

AB - One of the most diverse and productive marine ecosystems in the world are the corals, providing not only tourism but also an important economic contribution to the countries that have them on their coasts. Thanks to genome sequencing techniques, it is possible to identify the microorganisms that form the coral microbiome. The generation of large amounts of data, thanks to the low cost of sequencing since 2005, provides an opening for the use of artificial neural networks for the advancement of sciences such as biology and medicine. This work aims to predict the healthy microbiome present in samples of Mussismilia hispida coral, using machine learning algorithms, in which the algorithms SVM, Decision Tree, and Random Forest achieved a rate of 61%, 74%, and 72%, respectively. Additionally, it aims to identify possible microorganisms related to the disease in question in corals.

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Barque BM, Rodrigues PJS, de Paula Filho PL, Peixoto RS, de Assis Leite DC. Prediction of Health of Corals Mussismilia hispida Based on the Microorganisms Present in their Microbiome. In Pereira AI, Fernandes FP, Coelho JP, Mendes A, Pacheco MF, Lima J, editors, Optimization, Learning Algorithms and Applications - 3rd International Conference, OL2A 2023, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2024. p. 409-423. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-53025-8_28

Prediction of Health of Corals Mussismilia hispida Based on the Microorganisms Present in their Microbiome

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this