Machine learning approach to support taxonomic species discrimination based on helminth collections data
Abstract Background There are more than 300 species of capillariids that parasitize various vertebrate groups worldwide. Species identification is hindered because of the few taxonomically informative structures available, making the task laborious and genus definition controversial. Thus, its taxon...
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doaj-3020480787834b28a1af9dd6f38e147c2021-05-02T11:12:22ZengBMCParasites & Vectors1756-33052021-05-0114111510.1186/s13071-021-04721-6Machine learning approach to support taxonomic species discrimination based on helminth collections dataVictor Hugo Borba0Coralie Martin1José Roberto Machado-Silva2Samanta C. C. Xavier3Flávio L. de Mello4Alena Mayo Iñiguez5Laboratório de Biologia de Tripanosomatídeos-LABTRIP, Instituto Oswaldo Cruz, IOC-FIOCRUZUnité Molécules de Communication et Adaptation des Microorganismes (MCAM, UMR 7245), Muséum National d’Histoire Naturelle, CNRS, CP52Laboratório de Helmintologia Romero Lascasas Porto, Faculdade de Ciências Médicas, UERJLaboratório de Biologia de Tripanosomatídeos-LABTRIP, Instituto Oswaldo Cruz, IOC-FIOCRUZDepartamento de Engenheira Eletrônica e Computação, Universidade Federal do Rio de JaneiroLaboratório de Biologia de Tripanosomatídeos-LABTRIP, Instituto Oswaldo Cruz, IOC-FIOCRUZAbstract Background There are more than 300 species of capillariids that parasitize various vertebrate groups worldwide. Species identification is hindered because of the few taxonomically informative structures available, making the task laborious and genus definition controversial. Thus, its taxonomy is one of the most complex among Nematoda. Eggs are the parasitic structures most viewed in coprological analysis in both modern and ancient samples; consequently, their presence is indicative of positive diagnosis for infection. The structure of the egg could play a role in genera or species discrimination. Institutional biological collections are taxonomic repositories of specimens described and strictly identified by systematics specialists. Methods The present work aims to characterize eggs of capillariid species deposited in institutional helminth collections and to process the morphological, morphometric and ecological data using machine learning (ML) as a new approach for taxonomic identification. Specimens of 28 species and 8 genera deposited at Coleção Helmintológica do Instituto Oswaldo Cruz (CHIOC, IOC/FIOCRUZ/Brazil) and Collection de Nématodes Zooparasites du Muséum National d’Histoire Naturelle de Paris (MNHN/France) were examined under light microscopy. In the morphological and morphometric analyses (MM), the total length and width of eggs as well as plugs and shell thickness were considered. In addition, eggshell ornamentations and ecological parameters of the geographical location (GL) and host (H) were included. Results The performance of the logistic model tree (LMT) algorithm showed the highest values in all metrics compared with the other algorithms. Algorithm J48 produced the most reliable decision tree for species identification alongside REPTree. The Majority Voting algorithm showed high metric values, but the combined classifiers did not attenuate the errors revealed in each algorithm alone. The statistical evaluation of the dataset indicated a significant difference between trees, with GL + H + MM and MM only with the best scores. Conclusions The present research proposed a novel procedure for taxonomic species identification, integrating data from centenary biological collections and the logic of artificial intelligence techniques. This study will support future research on taxonomic identification and diagnosis of both modern and archaeological capillariids. Graphical abstracthttps://doi.org/10.1186/s13071-021-04721-6TaxonomyArtificial intelligenceSpecies identificationCapillaridaeParasite eggs |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Victor Hugo Borba Coralie Martin José Roberto Machado-Silva Samanta C. C. Xavier Flávio L. de Mello Alena Mayo Iñiguez |
spellingShingle |
Victor Hugo Borba Coralie Martin José Roberto Machado-Silva Samanta C. C. Xavier Flávio L. de Mello Alena Mayo Iñiguez Machine learning approach to support taxonomic species discrimination based on helminth collections data Parasites & Vectors Taxonomy Artificial intelligence Species identification Capillaridae Parasite eggs |
author_facet |
Victor Hugo Borba Coralie Martin José Roberto Machado-Silva Samanta C. C. Xavier Flávio L. de Mello Alena Mayo Iñiguez |
author_sort |
Victor Hugo Borba |
title |
Machine learning approach to support taxonomic species discrimination based on helminth collections data |
title_short |
Machine learning approach to support taxonomic species discrimination based on helminth collections data |
title_full |
Machine learning approach to support taxonomic species discrimination based on helminth collections data |
title_fullStr |
Machine learning approach to support taxonomic species discrimination based on helminth collections data |
title_full_unstemmed |
Machine learning approach to support taxonomic species discrimination based on helminth collections data |
title_sort |
machine learning approach to support taxonomic species discrimination based on helminth collections data |
publisher |
BMC |
series |
Parasites & Vectors |
issn |
1756-3305 |
publishDate |
2021-05-01 |
description |
Abstract Background There are more than 300 species of capillariids that parasitize various vertebrate groups worldwide. Species identification is hindered because of the few taxonomically informative structures available, making the task laborious and genus definition controversial. Thus, its taxonomy is one of the most complex among Nematoda. Eggs are the parasitic structures most viewed in coprological analysis in both modern and ancient samples; consequently, their presence is indicative of positive diagnosis for infection. The structure of the egg could play a role in genera or species discrimination. Institutional biological collections are taxonomic repositories of specimens described and strictly identified by systematics specialists. Methods The present work aims to characterize eggs of capillariid species deposited in institutional helminth collections and to process the morphological, morphometric and ecological data using machine learning (ML) as a new approach for taxonomic identification. Specimens of 28 species and 8 genera deposited at Coleção Helmintológica do Instituto Oswaldo Cruz (CHIOC, IOC/FIOCRUZ/Brazil) and Collection de Nématodes Zooparasites du Muséum National d’Histoire Naturelle de Paris (MNHN/France) were examined under light microscopy. In the morphological and morphometric analyses (MM), the total length and width of eggs as well as plugs and shell thickness were considered. In addition, eggshell ornamentations and ecological parameters of the geographical location (GL) and host (H) were included. Results The performance of the logistic model tree (LMT) algorithm showed the highest values in all metrics compared with the other algorithms. Algorithm J48 produced the most reliable decision tree for species identification alongside REPTree. The Majority Voting algorithm showed high metric values, but the combined classifiers did not attenuate the errors revealed in each algorithm alone. The statistical evaluation of the dataset indicated a significant difference between trees, with GL + H + MM and MM only with the best scores. Conclusions The present research proposed a novel procedure for taxonomic species identification, integrating data from centenary biological collections and the logic of artificial intelligence techniques. This study will support future research on taxonomic identification and diagnosis of both modern and archaeological capillariids. Graphical abstract |
topic |
Taxonomy Artificial intelligence Species identification Capillaridae Parasite eggs |
url |
https://doi.org/10.1186/s13071-021-04721-6 |
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