Identifying and testing of signatures for non-volatile biomolecules using tandem mass spectra

Ray R. Hashemi, Theresa M. Schafer, William G. Hinson, Jackson O. Lay

Research output: Contribution to book or proceedingConference articlepeer-review

1 Scopus citations

Abstract

Identification of volatile and semi-volatile molecules using traditional election ionization mass spectrometry has been successful. The major contributor to this success is the reproduceability of the mass spectra, which allow identification of components based on comparison of fragmentation patterns within very large databases. However, this approach is not useful for the identification of typical non-volatile biomolecules. Tandem mass spectrometry with collision induced dissociation (CID) has ' the potential to provide structure-specific fragmentation from non-volatile biomolecules. The recognition of these molecules based on CID is not an easy task, since the spectra generated for a given molecule are not as reproducible as in traditional electron ionization mass spectrometry. Also, the rules governing the formation of CID produced ions are not completely understood. In this study we investigate the use of the Kohonen Self-Organized Mapping (SOM) neural network to generate and test signatures (fragmentation patterns) for a given set of non-volatile biomolecules using spectra generated by tandem mass spectrometry with CID. The signatures then may be used as a discriminator for identifying unknown non-volatile biomolecules.

Original languageEnglish
Title of host publicationProceedings of the 1996 ACM Symposium on Applied Computing, SAC 1996
EditorsJanice H. Carroll, K. M. George, Jim Hightower, Dave Oppenheim
PublisherAssociation for Computing Machinery
Pages44-49
Number of pages6
ISBN (Electronic)0897918207
DOIs
StatePublished - Feb 18 1996
Event1996 ACM Symposium on Applied Computing, SAC 1996 - Philadelphia, United States
Duration: Feb 17 1996Feb 19 1996

Publication series

NameProceedings of the ACM Symposium on Applied Computing
VolumePart F128723

Conference

Conference1996 ACM Symposium on Applied Computing, SAC 1996
Country/TerritoryUnited States
CityPhiladelphia
Period02/17/9602/19/96

Keywords

  • Kohonen self organizing map and classification
  • Pattern generation
  • Tandem mass spectrometry

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