Bag of Peaks: Interpretation of NMR spectrometry

Bioinformatics

Volumn 25, Issue 2, 2009, Pages 258-264

  Brelstaff, Gavin ^a   Bicego, Manuele ^b,e   Culeddu, Nicola ^c   Chessa, Matilde ^d,f  

^a CRS4   (Italy)

^b UNIVERSITY OF SASSARI   (Italy)

^c CNR   (Italy)

^d Porto Conte Ricerche Srl   (Italy)

^e UNIVERSITY OF VERONA   (Italy)

^f Vincenzo Migaleddu Imaging srl   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CHILD; CLASSIFICATION ALGORITHM; CLASSIFIER; CLINICAL ARTICLE; CONTROLLED STUDY; HUMAN; HUMAN COMPUTER INTERACTION; INSULIN DEPENDENT DIABETES MELLITUS; INTERMETHOD COMPARISON; METABOLITE; MULTIDIMENSIONAL SCALING; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; SIGNAL NOISE RATIO; STATISTICAL MODEL; SUPPORT VECTOR MACHINE;

ALGORITHMS; CHILD; DIABETES MELLITUS, TYPE 1; HUMANS; NUCLEAR MAGNETIC RESONANCE, BIOMOLECULAR; PRINCIPAL COMPONENT ANALYSIS;

EID: 58349095392     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btn599     Document Type: Article

References (32)

1. Bishop,C. (1995) Neural Network for Pattern Recognition. Clarendon Press, Oxford.
2. Bretthorst,G.L. et al. (2005) Exponential parameter estimation (in NMR) using Bayesian probability theory. Concepts Magn. Reson. A, 27, 55-62.
3. Cattell,R. (1966) The screen test for the number of factors. Multivariate Behav. Res., 1, 245-276.
4. Cox,T. and Cox,M. (1994) Multidimensional Scaling. Chapman and Hall, London.
5. Cristianini,N. et al. (2002) Latent semantic kernels. J. Intell. Inf. Syst., 18, 127-152.
6. Csurka,G. et al. (2004) Visual categorization with bags of keypoints. In Proceedings of the Workshop Pattern Recognition and Machine Learning in Computer Vision. Grenoble, France.
7. Davies,D. and Bouldin,D. (1979) A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell., pp. 224-227.
8. Duin,R. et al. (2004) Prtools4, amatlab toolbox for pattern recognition. Delft University of Technology.
9. Ernst,R. et al. (1990) Principles of Nuclear Magnetic Resonance in One and Two Dimensions. Clarendon Press, Oxford, England.
10. Felsenstein,J. (1985) Confidence limits on phylogenies: An approach using the bootstrap. Evolution, 39, 783-791.
11. Fukanaga,K. (1990) Introduction to Statistical Pattern Recognition 2nd edn. Academic press, San Diego.
12. Hartigan,J. (1975) Clustering Algorithms. John Wiley & Sons, Oxford, England.
13. Hastie,T. et al. (2001) The Elements of Statistical Learning Springer Verlag.
14. Jain,A. and Dubes,R. (1988) Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, NJ, USA.
15. Jain,A. et al. (1999) Data clustering: A review. ACM Comput. Surv., 31, 264-323.
16. Joachims,T. (1998) Text categorization with support vector machines: learning with many relevant features. In Proceedings of the European Conf. Machine Learning, Springer Verlag, Heidelberg, Germany, pp. 137-142.
17. Jollife,I.T. (1986) Principal Component Analysis. Springer Verlag, New York.
18. Keun,H.C. (2006) Metabonic modeling of drug toxicity. J. Pharmacol. Ther., 109, 92-106.
19. Kohavi,R. and John,G. (1997) Wrappers for feature subset selection. Artif. Intell., 97, 273-324.
20. Kruskal,J. (1997) Multidimensional scaling and other methods for discovering structure. In Statistical Methods for Digital Computers John Wiley & Sons, New York, pp. 296-339.
21. Lazebnik,S. et al. (2006) Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vol. 2. Association for Computing Machinery, NY, USA, pp. 2169-2178.
22. Lefebvre,B. et al. (2004) Intelligent bucketing for metabonomics - part 1. In Metabolic Profiling: Pathways in Discovery. ACD Labs, Toronto, Ontario, Canada.
23. Lindon,J. et al. (2003) Contemporary issues in toxicology - the role of metabonomics in toxicology and its evaluation by the COMET project. Toxicol. Appl. Pharmacol., 187, 137-146.
24. Lindon,J.C. et al. (2001) Pattern recognition methods and applications in biomedical magnetic resonance. Prog. Nucl. Magn. Reson. Spectrosc., 39, 1-40.
25. Lindon,J. et al. (2007) The Handbook of Metabonomics and Metabolomics. Elsevier, Amsterdam, Holland.
26. Lodhi,H. et al. (2001) Text classification using string kernels. In Advances in Neural Information Processing Systems, Vol. 13. MIT Press, Cambridge, MA, USA.
27. Schölkopf,B. and Smola,A. (2002) Learning with Kernels. MIT Press, Cambridge, MA.
28. Schorn,C. (2002) NMR Spectroscopy: Data Acquisition. Wiley-VCH Verlag GmbH & Co., Weinheim, Germany.
29. Stoyanova,R. and Brown,T. (2001) NMR spectral quantitation by principal component analysis. NMR Biomed., 14, 271-277.
30. Theodoridis,S. and Koutroumbas,K. (1999) Pattern Recognition. Academic Press, New York, USA.
31. Tibshirani,R. et al. (2004) Sample classification from protein mass spectrometry, by 'peak probability contrasts'. Bioinformatics 20, 3034-3044.
32. Zhang,J. et al. (2007) Local features and kernels for classifcation of texture and object categories: A comprehensive study. Int. J. Comput. Vis., 73, 213-238.