Machine learning algorithms accurately identify free-living marine nematode species.

ABSTRACT:

Background

Identifying species, particularly small metazoans, remains a daunting challenge and the phylum Nematoda is no exception. Typically, nematode species are differentiated based on morphometry and the presence or absence of certain characters. However, recent advances in artificial intelligence, particularly machine learning (ML) algorithms, offer promising solutions for automating species identification, mostly in taxonomically complex groups. By training ML models with extensive datasets of accurately identified specimens, the models can learn to recognize patterns in nematodes' morphological and morphometric features. This enables them to make precise identifications of newly encountered individuals. Implementing ML algorithms can improve the speed and accuracy of species

SUBMITTER: Brito de Jesus S 

PROVIDER: S-EPMC10569207 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature