Introduction to artificial neural systems
Introduction to artificial neural systems
Prediction capability of neural networks trained by Monte-Carlo paradigm
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
A neural network as a quality control monitor of an intelligent system
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
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Identification of volatile and semi-volatile molecules using traditional electron 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 nonvolatile 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.