TY - GEN
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.
KW - Coral reef
KW - Machine learning algorithm
KW - Microbiome
UR - http://www.scopus.com/inward/record.url?scp=85204309627&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-53025-8_28
DO - 10.1007/978-3-031-53025-8_28
M3 - Conference contribution
AN - SCOPUS:85204309627
SN - 9783031530241
T3 - Communications in Computer and Information Science
SP - 409
EP - 423
BT - Optimization, Learning Algorithms and Applications - 3rd International Conference, OL2A 2023, Revised Selected Papers
A2 - Pereira, Ana I.
A2 - Fernandes, Florbela P.
A2 - Coelho, Joao P.
A2 - Mendes, Armando
A2 - Pacheco, Maria F.
A2 - Lima, Jose
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2023
Y2 - 27 September 2023 through 29 September 2023
ER -