Feature scoring by mutual information for classification of mass spectra

Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006

Volumn , Issue , 2006, Pages 557-564

  Krier, C ^a   Francois D ^b   Wertz, V ^b   Verleysen, M ^a  

^a UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

^b CESAME Research Center   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FUZZY LOGIC; MASS SPECTROMETERS; MASS SPECTROMETRY;

CLASSIFICATION PERFORMANCE; FEATURE GROUPS; FORWARD SELECTION; INTERPRETABILITY; MASS SPECTRA; MODEL FREE; MUTUAL INFORMATIONS; RELEVANT FEATURES;

CLASSIFICATION (OF INFORMATION);

EID: 85026190757     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1142/9789812774118_0079     Document Type: Conference Paper

References (13)

1. BL. Adam et al., Serum protein fingerprinting coupled with a patternmatching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men, Cancer Research, 62, 3609 (2002).
2. G. Ball et al., An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers. Bioinformatics, 18, 395 (2002).
3. H. Zhou et al., Quantitative proteome analysis by solid-phase isotope tagging and mass spectrometry, Nature Biotechnology, 20, 512 (2002).
4. H. Liu, J. Li and L. Wong., A Comparative Study on Feature Selection and Classication Methods Using Gene Expression Proles and Proteomic Patterns, Genome Informatics 13,51 (2002)
5. E. Petricoin III et al., Use of proteomic patterns in serum to identify ovarian cancer, The Lancet, 359 572 (2002)
6. R H. Lilien, H. Farin, B. R Donald, Probabilistic Disease Classification of Expression-Dependent Proteomic data from Mass Spectrometry of Human Serum, Journal of Computational Biology 10(6), 925 (2003).
7. C.E. Shannon, W. Weaver, The Mathematical Theory of Communication, University of Illinois Press, Urbana, IL, 1949.
8. D.W. Scott, Multivariable Density Estimation: Theory, Practice, and Visualization, Wiley, New-York, 1992.
9. RL. Dobrushin, A simplified method of experimental evaluation of the entropy of stationary sequence, Theory Prob. Appl. 3(4), 462 (1958).
10. O. Vasicek, Test for normality based on sample entropy, J. Royal Statist. Soc. B38, 54 (1976).
11. A. Kraskov, H. Stögbauer, P. Grassberger, Estimating mutual information, Phys. Rev. E69:066138 (2004).
12. F. Rossi, A. Lendasse, D. Françrois, V. Wertz, M. Verleysen, Mutual information for the selection of relevant variables in spectrometric nonlinear modeling, Chemometrics & Intelligent Lab. Systems, 80, 215-226 (2006).
13. http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp