Metabolite identification and molecular fingerprint prediction through machine learning

Bioinformatics

Volumn 28, Issue 18, 2012, Pages 2333-2341

  Heinonen, Markus ^a,b   Shen, Huibin ^a   Zamboni, Nicola ^c   Rousu, Juho ^c,d  

^a UNIVERSITY OF HELSINKI   (Finland)

^b HELSINKI INSTITUTE FOR INFORMATION TECHNOLOGY   (Finland)

^c ETH ZURICH   (Switzerland)

^d AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL INTELLIGENCE; CHEMICAL DATABASE; METABOLOMICS; METHODOLOGY; TANDEM MASS SPECTROMETRY;

ARTIFICIAL INTELLIGENCE; DATABASES, CHEMICAL; METABOLOMICS; TANDEM MASS SPECTROMETRY;

EID: 84866437252     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts437     Document Type: Article

References (22)

1. Bakir,G. (2007) Predicting Structured Data. MIT Press.
2. Böcker,S. et al. (2009) Sirius: decomposing isotope patterns for metabolite identification. Bioinformatics., 25, 1-9.
3. Breiman,L. et al. (1984) Classification and Regression Trees. Chapman and Hall/CRC.
4. Curry,B. and Rumelhart,D. (1990) Msnet: a neural network that classifies mass spectra. Tetrahedron Com. Methodol., 3, 213-237.
5. Dayringer,H. et al. (1976) Computer-aided interpretation of mass spectra. Information on substructural probabilities form stirs. Organic Mass Spectrometry, 11, 529-542.
6. Dworzanski,J.P. et al. (2004) Identification of bacteria using tandem mass spectrometry combined with a proteome database and statistical scoring. Anal. Chem., 76, 2355-2366.
7. Heinonen,M. et al. (2008) Fid: a software for ab initio structural identification of product ions from tandem mass spectrometric data. Rapid Comm. Mass. Spectrom., 22, 3043-3052.
8. Horai,H. et al. (2010) Massbank: a public repository for sharing mass spectral data for life sciences. J. Mass Spectrom, 45, 703-714.
9. Jebara,T. et al. (2004) Probability product kernels. J. Machine Learn. Res., 5, 819-844.
10. Kanehisa,M. et al. (2006) From genomics to chemical genomics: new developments in kegg. Nuclead Acids Res., 34, 354-357.
11. Kell,D. (2004) Metabolomics and systems biology: making sense of the soup. Curr. Opin. Microbiol, 7, 296-307.
12. Kondor,R. and Jebara,T. (2003) A kernel between sets of vectors. ICML, Proceedings of the Twentieth International Conference on Machine learning (ICML-2003), Washington DC.
13. McLafferty,F. (1983) Tandem Mass Spectrometry. Wiley, New York.
14. Neumann,S. and Böcker,S. (2010) Computational mass spectrometry for metabolomics: Identification of metabolites and small molecules. Anal. Bioanal. Chem., 398, 2779-88.
15. Oberacher,H. et al. (2009) On the instrument and the inter-laboratory transferability of a tandem mass spectral reference library: 2. optimization and characterization of the search algorithm. J. Mass Spectrom., 44, 494-502.
16. Pavlic,M. et al. (2006) Combined use of esi-qqtof-ms and esi-qqtof-ms/ms with mass-spectral library search for qualittative analysis of drugs. Anal. Bioanal. Chem., 386, 69-82.
17. Shawe-Taylor,J. and Christianini,N. (2004) Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge, United Kingdom.
18. Steffen,A. et al. (2009) Comparison of molecular fingerprint methods on the basis of biological profile data. J. Chem. Inf. Model., 49, 338-347.
19. Stein,S.E. (1994) Estimating probabilities of correct identification from results of mass spectral library searches. J. Am. Soc. Mass. Spectrom., 5, 316-323.
20. Wang,Y. et al. (2009) Pubchem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res., 37, W623-W633.
21. Werner,E. et al. (2008) Mass spectrometry for the identification of the discriminating signals from metabolomics: current status and future trends. J. Chromatogr.B, 871, 143-164.
22. Wolf,S. et al. (2010) In silico fragmentation for computer assisted identification of metabolite mass spectra. BMC Bioinformatics, 11, 148.