Semi-supervised machine learning for automated species identification by collagen peptide mass fingerprinting.

ABSTRACT:

Background

Biomolecular methods for species identification are increasingly being utilised in the study of changing environments, both at the microscopic and macroscopic levels. High-throughput peptide mass fingerprinting has been largely applied to bacterial identification, but increasingly used to identify archaeological and palaeontological skeletal material to yield information on past environments and human-animal interaction. However, as applications move away from predominantly domesticate and the more abundant wild fauna to a much wider range of less common taxa that do not yet have genetically-derived sequence information, robust methods of species identification and biomarker selection need to be determined.

Results

Here we developed a supervised machine learning a

SUBMITTER: Gu M 

PROVIDER: S-EPMC6019507 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature