Comparison of discrimination methods for peptide classification in tandem mass spectrometry

Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04

Volumn , Issue , 2004, Pages 160-167

  Bonner, Anthony ^a   Liu, Han ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

Bioinformatics; Data mining; Machine learning; Peptide classification; Proteomics; Tandem mass spectrometry

Indexed keywords

AMINO ACIDS; DATA MINING; DATABASE SYSTEMS; LEARNING SYSTEMS; MASS SPECTROMETRY; PROBLEM SOLVING; REGRESSION ANALYSIS; TISSUE;

BIOINFORMATICS; PEPTIDE CLASSIFICATION; PROTEOMICS; TANDEM MASS SPECTROMETRY;

PROTEINS;

EID: 17044415202     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Aebersold, R., Mann, M. Mass spectrometry-based proteomics, Nature 422, pp. 198-207, 2003.
2. Bonner, A., Liu, H. Development and Evaluation of Methods for Predicting Protein Levels and Peptide Peak Intensities from Tandem Mass Spectrometry Data. Submitted for publication. Available at www.cs.toronto.edu/~bonner/papers.html.
3. Bonner, A., Liu, H. Canonical Correlation, an Approximation, and its Application to the Mining of Tandem Mass Spectrometry Data. Submitted for publication. Available at www.cs.toronto.edu/~bonner/papers.html.
4. Duda, R., Hart, P., Stork, D. Pattern Classification, second edition. Wiley-Interscience, 2001.
5. Dudoit, S., Fridlyand, J., and Speed, T.P. Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97(457), 77-87.
6. Elias, J.E., Gibbons, F.D., King, O.D., Roth, F., Gygi, S.P. Intensity-based protein identification by machine learning from a library of tandem mass spectra. Nature, biotechnology, Volume 22 Number 2, 2004.
7. Fisher, R.A. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179-188.
8. Gay, S., Binz, P.A., Hochstrasser, D.F., Appel, R.D. Peptide mass fingerprinting peak intensity prediction: extracting knowledge from spectra. Proteomics 2, pp. 1374-1391, 2002.
9. Hastie, H., Tibshirani, R., Friedman, J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer Series in Statistics, 2001.
10. Jordan, M. Why the Logistic Function? A tutorial discussion on probabilities and neural networks. Computational Cognitive Science Technical Report 9503, Massachusetts Institute of Technology, 1995.
11. Kussmann, M., Nordhoff,E., Rahbek-Nielsen, H., Haebel, S., et al. Journal of Mass Spectrom 32, pp. 593-601, 1997.
12. Mann, M., Hendrickson, R.C., Pandey, A. Analysis of proteomes by mass spectrometry. Annu. Rew. Biochem. 70, pp. 437-473, 2001.
13. Mardia, K.V., J.T.Kent, and J.M.Bibby Multivariate Analysis. Academic Press, Inc., San Diego, 1979.
14. McLachlan, G.J. Discriminant analysis and Statistical pattern recognition. Wiley, New York. 1992.
15. Pachter, L., Alexandersson, M., Cawley, S. Applications of Hidden Markov Models to Alignment and Gene Finding Problems. Proceedings of the Fifth Annual Conference on Computational Biology, pp. 241-248, 2001. ACM Press.
16. Pandey, A., Mann, M. Proteomics to study genes and genomes. Nature 405, pp. 837-846, 2000.
17. Poritz, A.B. Linear Predictive Hidden Markov Models and the Speech Signal. Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, pp. 1291-1294, 1982.
18. Purves, R. W., Gabryelski, W., Li, L., Rapic. Commun. Mass Spectrom 2, pp. 695-700, 1998.
19. Rice, J. Mathematical Statistics and Data Analysis, second edition. Duxbury Press, 1995.
20. Ripley, B.D. Pattern recognition and neural networks. Cambridge University Press, Cambridge, New York, 1996
21. Thosma, K., Khaled, R., Dragan, Radulovic. PRIM, a Generic Large Scale Proteomics investigation Strategy for Mammals. Molecular & Cellular Proteomics 2.1, 2003.