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 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 characte

SUBMITTER: Borba VH 

PROVIDER: S-EPMC8088700 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature