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Machine learning approach to support taxonomic species discrimination based on helminth collections data

Overview of attention for article published in Parasites & Vectors, May 2021
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  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

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7 X users

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Title
Machine learning approach to support taxonomic species discrimination based on helminth collections data
Published in
Parasites & Vectors, May 2021
DOI 10.1186/s13071-021-04721-6
Pubmed ID
Authors

Victor Hugo Borba, Coralie Martin, José Roberto Machado-Silva, Samanta C. C. Xavier, Flávio L. de Mello, Alena Mayo Iñiguez

Abstract

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. 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. 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. 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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Professor 4 11%
Other 4 11%
Student > Ph. D. Student 4 11%
Student > Doctoral Student 3 8%
Researcher 3 8%
Other 5 14%
Unknown 14 38%
Readers by discipline Count As %
Engineering 4 11%
Agricultural and Biological Sciences 4 11%
Medicine and Dentistry 3 8%
Biochemistry, Genetics and Molecular Biology 2 5%
Veterinary Science and Veterinary Medicine 2 5%
Other 6 16%
Unknown 16 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 May 2021.
All research outputs
#6,950,859
of 24,833,726 outputs
Outputs from Parasites & Vectors
#1,523
of 5,843 outputs
Outputs of similar age
#142,039
of 433,390 outputs
Outputs of similar age from Parasites & Vectors
#40
of 143 outputs
Altmetric has tracked 24,833,726 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 5,843 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 433,390 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 143 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.