title: Identification of structural features from mass spectrometry using a neural network approach: application to trimethylsilyl derivatives used for medical diagnosis.
abstract: An artificial neural network (ANN) has been trained to recognize the presence or absence of specific structural features (SF) in trimethylsilyl derivatives of organic acids from their mass spectra. The input vector is constructed without knowledge of the molecular ion, which is generally not observed in the spectra of these compounds. The results are used in conjunction with a classical search in a spectral library to identify organic acids in biological fluids for rapid acidemias diagnosis.
authors.affiliation: LARTIC and Chimie organique du fluor, Université de Nice-Sophia Antipolis, Nice, France.
authors.forename: A
authors.initials: A
authors.lastname: Eghbaldar
authors.forename: T P
authors.initials: TP
authors.lastname: Forrest
authors.forename: D
authors.initials: D
authors.lastname: Cabrol-Bass
authors.forename: A
authors.initials: A
authors.lastname: Cambon
authors.forename: J M
authors.initials: JM
authors.lastname: Guigonis
mesh_terms: D000138:Acidosis / Q000175:diagnosis; D003936:Diagnosis, Computer-Assisted; D008401:Gas Chromatography-Mass Spectrometry; D006801:Humans; D016571:Neural Networks, Computer; D014297:Trimethylsilyl Compounds / Q000737:chemistry*
chemical_list: D014297:Trimethylsilyl Compounds