Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties.

ABSTRACT: Functional diversity rather than species richness is critical for the understanding of ecological patterns and processes. This study aimed to develop novel integrated analytical strategies for the functional characterization of fish diversity based on the quantification, prediction and integration of the chemical and physical features in fish muscles. Machine learning models with an improved random forest algorithm applied on 1867 muscle nuclear magnetic resonance spectra belonging to 249 fish species successfully predicted the mobility patterns of fishes into four categories (migratory, territorial, rockfish, and demersal) with accuracies of 90.3-95.4%. Markov blanket-based feature selection method with an ecological-chemical-physical integrated network based on the Bayesian network infer

SUBMITTER: Wei F 

PROVIDER: S-EPMC7881121 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature