title: SVM-based spectral matching for metabolite identification.
abstract: Mass spectrometry-based metabolomics is getting mature and playing an ever important role in the systematic understanding of biological process in conjunction with other members of "-omics" family. However, the identification of metabolites in untargeted metabolomics profiling remains a challenge. In this paper, we propose a support vector machine (SVM)-based spectral matching algorithm to combine multiple similarity measures for accurate identification of metabolites. We compared the performance of this approach with several existing spectral matching algorithms on a spectral library we constructed. The results demonstrate that our proposed method is very promising in identifying metabolites in the face of data heterogeneity caused by different experimental parameters and platforms.
authors.affiliation: Department of Electrical and Computer Engineering at Virginia Polytechnic Institute and State University, Falls Church, VA 22043 USA. zhoubin@vt.edu
authors.forename: Bin
authors.initials: B
authors.lastname: Zhou
authors.forename: Amrita K
authors.initials: AK
authors.lastname: Cheema
authors.forename: Habtom W
authors.initials: HW
authors.lastname: Ressom
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D020869:Gene Expression Profiling; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D055442:Metabolome / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010449:Peptide Mapping / Q000379:methods*; D020543:Proteome / Q000032:analysis*
chemical_list: D020543:Proteome