Published in

Oxford University Press (OUP), Bioinformatics, 12(30), p. i157-i164

DOI: 10.1093/bioinformatics/btu275

Links

Tools

Export citation

Search in Google Scholar

Metabolite identification through multiple kernel learning on fragmentation trees

Journal article published in 2014 by Huibin Shen, Kai Dührkop, Sebastian Böcker ORCID, Juho Rousu
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Upload
Green circle
Postprint: archiving allowed
Upload
Red circle
Published version: archiving forbidden
Policy details
Data provided by SHERPA/RoMEO

Abstract

Motivation: Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints; predicted fingerprints, in turn, can be used to score candidate molecular structures.