A demonstration of unsupervised machine learning in species delimitation.

ABSTRACT: One major challenge to delimiting species with genetic data is successfully differentiating population structure from species-level divergence, an issue exacerbated in taxa inhabiting naturally fragmented habitats. Many fields of science are now using machine learning, and in evolutionary biology supervised machine learning has recently been used to infer species boundaries. These supervised methods require training data with associated labels. Conversely, unsupervised machine learning (UML) uses inherent data structure and does not require user-specified training labels, potentially providing more objectivity in species delimitation. In the context of integrative taxonomy, we demonstrate the utility of three UML approaches (random forests, variational autoencoders, t-distributed stochasti

SUBMITTER: Derkarabetian S 

PROVIDER: S-EPMC6880864 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature