title: Metabolite identification through multiple kernel learning on fragmentation trees.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  Here, we combine fragmentation tree computations with kernel-based machine learning to predict molecular fingerprints and identify molecular structures. We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the top position of the candidates list.
authors.affiliation: Department of Information and Computer Science, Aalto University, Espoo, Finland, Helsinki Institute for Information Technology, Espoo, Finland and Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, GermanyDepartment of Information and Computer Science, Aalto University, Espoo, Finland, Helsinki Institute for Information Technology, Espoo, Finland and Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, Germany.
authors.forename: Huibin
authors.initials: H
authors.lastname: Shen
authors.affiliation: Department of Information and Computer Science, Aalto University, Espoo, Finland, Helsinki Institute for Information Technology, Espoo, Finland and Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, Germany.
authors.forename: Kai
authors.initials: K
authors.lastname: Dührkop
authors.affiliation: Department of Information and Computer Science, Aalto University, Espoo, Finland, Helsinki Institute for Information Technology, Espoo, Finland and Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, Germany.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Böcker
authors.affiliation: Department of Information and Computer Science, Aalto University, Espoo, Finland, Helsinki Institute for Information Technology, Espoo, Finland and Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, GermanyDepartment of Information and Computer Science, Aalto University, Espoo, Finland, Helsinki Institute for Information Technology, Espoo, Finland and Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, Germany.
authors.forename: Juho
authors.initials: J
authors.lastname: Rousu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D055432:Metabolomics / Q000379:methods*; D015394:Molecular Structure; D012984:Software; D053719:Tandem Mass Spectrometry / Q000379:methods*