Identification of structural features from mass spectrometry using a neural network approach: Application to trimethylsilyl derivatives used for medical diagnosis

Journal of Chemical Information and Computer Sciences

Volumn 36, Issue 4, 1996, Pages 637-643

  Eghbaldar, Ahmad ^a   Forrest, Thomas P ^b   Cabrol Bass, Daniel ^a   Cambon, Aimé ^a   Guigonis, Jean Marie ^a  

^a UNIVERSITÉ CÔTE D'AZUR   (France)

^b DALHOUSIE UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DIAGNOSTIC AGENT; TRIMETHYLSILYL DERIVATIVE;

ACIDOSIS; ARTICLE; ARTIFICIAL NEURAL NETWORK; CHEMISTRY; COMPUTER ASSISTED DIAGNOSIS; HUMAN; MASS FRAGMENTOGRAPHY;

ACIDOSIS; DIAGNOSIS, COMPUTER-ASSISTED; GAS CHROMATOGRAPHY-MASS SPECTROMETRY; HUMANS; NEURAL NETWORKS (COMPUTER); TRIMETHYLSILYL COMPOUNDS;

EID: 0030188292     PISSN: 00952338     EISSN: None     Source Type: Journal    
DOI: 10.1021/ci9501296     Document Type: Article

References (17)

1. Jellum, E.; Kvittingen, A. A.; Stokke, O. Mass Spectrometry in Diagnosis of Metabolic Disorders. Biomed. Environ. Mass Spec. 1988, 16, 57-62.
2. Williams, J. C. Organic Acidurias in Children. Spectra 1985, 10, 2, 4-6.
3. Jellum, E. Profiling of human body fluids in healthy and diseased states using gas chromatography and mass spectrometry with special reference to organic acids. J. Chromatogr. 1977, 143, 427-462.
4. Curry, B.; Rumelhart, D. E. MSnet: A Neural Network That Classifies Mass Spectra, Tetrahedron Comput. Method. 1990, 3, 3/4, 213-237.
5. Lohninger, H. Classification of Mass Spectral Data Using Neural Networks Software development in Chemistry; Gmehling J., Ed.; Springer Verlag: Berlin Heidelberg, 1991; Vol. 5, pp 159-168.
6. Lohninger, H.; Stanel F. Comparing the performance of neural networks to well established methods of multivariate data analysis: the classification of mass spectral data. Fresenius J. Anal. Chem. 1992, 334, 186-189.
7. Werther, W.; Lohninger, H.; Stanlcl, F.; Varmuza, K. Classification of Mass Spectra - A Comparison of Yes/No Classification Methods for the Recognition of Simple Structural Properties. Chemom. Intell. Lab. Syst. 1994, 22, 1, 63-76.
8. Van Est, Q. C.; Schoenmakers, P. J.; Smits, J. R. M.; Nijssen, W. P. M. Practical Implementation of Neural Networks for the interpretation of Infrared Spectra. Vib. Spectrosc. 1993, 4, 263-272.
9. Cabrol-Bass, D.; Cachet, C.; Cleva, C.; Eghbaldar, A.; Forrest, T. P. Application pratique des réseaux neuro mimétiques aux données spectroscopiques (infrarouge et masse) en vue de l'élucidation structurale. Can. J. Chem. 1995, 73, 1412-1426.
10. Zupan, A.; Gasteiger, J. Neural networks: A new method for solving chemical problems or just a passing phase. Anal. Chim. Acta 1991, 248, 1-30.
11. Lacy, M. E. Neural Network technology and its Application in Chemical Research. Tetrahedron Comput. Method. 1990, 3,3/4, 119-128.
12. Jansson, P. A. Neural Networks: An Overview. Anal. Chem. 1991, 63,6, 357A-361A.
13. Leighton R. R. The Aspirin/Migraines Software Tools. Technical Report MP-91 W00050; The MITRE Corp.
14. Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. Learning Representations by Back-Propagating Errors. Nature 1986, 323, 533-536.
15. Leonard, J.; Kramer, M. A. Improvement of the Backpropagation Algorithm for Training Neural Networks. Comput. Chem. Eng. 1990, 14, 3, 337-341.
16. Sbirrazzuoli, N.; Cachet, C.; Cabrol-Bass, D.; Forrest T. P. Indices for the Evaluation of Neural Network Performance as Classifier: Application to Structural Elucidation in Infrared Spectroscopy. Neural Comput., Applic. 1993, 1, 229-239.
17. Ricard, D.; Cachet, C.; Cabrol-Bass, D.; Forrest, T. P. Neural Network Approach to Structural Feature Recognition form Infrared Spectra. J. Chem. Inf. Comput. Sci. 1993, 33, 2, 202-210.