Machine learning models accurately predict clades of proteocephalidean tapeworms (Onchoproteocephalidea) based on host and biogeographical data

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of North Carolina at Charlotte (UNC Charlotte)-
Autor(es): dc.contributorBiology Centre of the Czech Academy of Sciences-
Autor(es): dc.contributorNatural History Museum-
Autor(es): dc.contributorFederal Rural University of Rio de Janeiro (UFRRJ)-
Autor(es): dc.creatorVieira Alves, Philippe-
Autor(es): dc.creatorda Silva, Reinaldo José-
Autor(es): dc.creatorScholz, Tomáš-
Autor(es): dc.creatorde Chambrier, Alain-
Autor(es): dc.creatorLuque, José Luis-
Autor(es): dc.creatorDuchenko, Anastasiia-
Autor(es): dc.creatorJanies, Daniel-
Autor(es): dc.creatorJacob Machado, Denis-
Data de aceite: dc.date.accessioned2025-08-21T19:05:32Z-
Data de disponibilização: dc.date.available2025-08-21T19:05:32Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1111/cla.12610-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/297277-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/297277-
Descrição: dc.descriptionProteocephalids are a cosmopolitan and diverse group of tapeworms (Cestoda) that have colonized vertebrate hosts in freshwater and terrestrial environments. Despite the ubiquity of the group, key macroevolutionary processes that have driven the group's evolution have yet to be identified. Here, we review the phylogenetic relationships of proteocephalid tapeworms using publicly available (671) and newly generated (91) nucleotide sequences of the nuclear RNA28S and the mitochondrial MT-CO1 for 537 terminals. The main tree search was carried out under the parsimony optimality criterion, analysing different gene alignments simultaneously. Interestingly, we were not able to recover monophyly of the Proteocephalidae. Additionally, it was difficult to reconcile the tree with host and biogeographical data using traditional character optimization strategies in two dimensions. Therefore, we investigated if host and biogeographical data can be correlated with the parasite clades in a multidimensional space–thus considering multiple layers of information simultaneously. To that end, we used random forests (a class of machine learning models) to test the predictive potential of combined (not individual) host and biogeographical data in the context of the proteocephalid tree. Our resulting models can correctly place 88.85% (on average) of the terminals into eight representative clades. Moreover, we interactively increased the levels of clade perturbation probability and confirmed the expectation that model accuracy negatively correlates with the degree of clade perturbation. Our results show that host and biogeographical data can accurately predict proteocephalid clades in multidimensional space, even though they are difficult to optimize in the parasite tree. These results agree with the assumption that the evolution of proteocephalids is not independent of host and biogeography, and both may provide external support for our tree.-
Descrição: dc.descriptionInstitute of Biosciences Department of Biodiversity and Biostatistics Section of Parasitology São Paulo State University (UNESP), Rua Professor Doutor Antonio Celso Wagner Zanin 250-
Descrição: dc.descriptionCenter for Computational Intelligence to Predict Health and Environmental Risks (CIPHER) University of North Carolina at Charlotte (UNC Charlotte), 9331 Robert D. Snyder Rd-
Descrição: dc.descriptionInstitute of Parasitology Biology Centre of the Czech Academy of Sciences, Branišovská 31-
Descrição: dc.descriptionDepartment of Invertebrates Natural History Museum, CH-1211-
Descrição: dc.descriptionDepartment of Animal Parasitology Federal Rural University of Rio de Janeiro (UFRRJ), Rod. BR 465, km 7, RJ-
Descrição: dc.descriptionDepartment of Bioinformatics and Genomics College of Computing and Informatics University of North Carolina at Charlotte (UNC Charlotte), 9331 Robert D. Snyder Rd-
Descrição: dc.descriptionInstitute of Biosciences Department of Biodiversity and Biostatistics Section of Parasitology São Paulo State University (UNESP), Rua Professor Doutor Antonio Celso Wagner Zanin 250-
Idioma: dc.languageen-
Relação: dc.relationCladistics-
???dc.source???: dc.sourceScopus-
Título: dc.titleMachine learning models accurately predict clades of proteocephalidean tapeworms (Onchoproteocephalidea) based on host and biogeographical data-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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