Publicação
Prediction of Health of Corals Mussismilia hispida Based on the Microorganisms Present in their Microbiome
| Resumo: | 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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| Autores principais: | Barque, Barry Malick |
| Outros Autores: | Rodrigues, Pedro João; Paula Filho, Pedro Luiz de; Peixoto, Raquel Silva; Leite, Deborah Catharine de Assis |
| Assunto: | Coral reef Microbiome Machine learning algorithm |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | 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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