Cancer informatics by prototype networks in mass spectrometry

References 

1. Pusch W, Flocco M, Leung S, Thiele H, Kostrzewa M. Mass spectrometry-based clinical proteomics. Pharmacogenomics. 2003;4:463–476
   • MEDLINE
   • CrossRef
2. Fiedler G, Baumann S, Leichtle A, Oltmann A, Kase J, Thiery J, et al. Standardized peptidome profiling of human urine by magnetic bead separation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Clinical Chemistry. 2007;53(3):421–428
   • MEDLINE
   • CrossRef
3. Schäffeler E, Zanger U, Schwab ME. Magnetic bead based human plasma profiling discriminate acute lymphatic leukaemia from non-diseased samples. In: Proceedings of the 52st American Society of Mass Spectrometry Conference on Mass Spectrometry and Allied Topics (ASMS). New York, NY: Wiley; 2004;p. TPV 420
4. Schipper R, loof A, de Groot J, Harthoorn L, van Heerde W, Dransfield E. Salivary protein/peptide profiling with seldi-tof-ms. Annals of the New York Academy of Science. 2007;1098:498–503
   • MEDLINE
5. Guerreiro N, Gomez-Mancilla B, Charmont S. Optimization and evaluation of seldi-tof mass spectrometry for protein profiling of cerebrospinal fluid. Proteome science. 2006;4:7
6. Bishop C. Pattern recognition and machine learning. New York, NY: Springer Science+Business Media, LLC; 2006;
7. Hammer B, Strickert M, Villmann T. Supervised neural gas with general similarity measure. Neural Processing Letters. 2005;21(1):21–44
8. Villmann T, Schleif F-M, Hammer B. Supervised neural gas and relevance learning in learning vector quantisation. In:  Yamakawa T editors. Proceedings of the 4th Workshop on Self-Organizing Maps (WSOM) 2003. Kyushu Institute of Technology on CD-ROM (C) 2003 WSOM’03 Organizing Committee, Hibikino, Kitakyushu, Japan; 2003;p. 47–52
9. Schleif F-M, Clauss U, Villmann T, Hammer B. Supervised relevance neural gas and unified maximum separability analysis for classification of mass spectrometric data. In:  Wani MA,  Cios KJ,  Hafeez K editor. Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. Los Alamitos, CA, USA: IEEE Press; 2004;p. 374–379
10. Villmann T, Schleif F-M, Hammer B. Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks. 2005;19(15):610–622
11. Villmann T, Schleif F-M, Hammer B, Kostrzewa M. Exploration of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings in Bioinformatics. 2008;9(2):129–143
    • CrossRef
12. Schleif F-M, Villmann T, Hammer B. Analysis of proteomic spectral data by multi resolution analysis and self-organizing-maps. In: Proceedings of the 7th International Workshop on Fuzzy Logic and Applications (WILF) 2007. Berlin, Heidelberg, Germany: Springer; 2007;p. 563–570
13. Schleif F-M, Hammer B, Villmann T. Supervised neural gas for functional data and its application to the analysis of clinical proteom spectra. In: In Proceedings of the 9th International Work-Conference on Artificial Neural Networks (IWANN) 2007. Berlin, Heidelberg, Germany: Springer; 2007;p. 1036–1044
14. Vovk V, Gammerman A, Shafer G. Alorithmic learning in a random world. New York: Springer; 2005;
15. Gammerman A, Vovk V. Hedging predictions in machine learning. The Computer Journal. 2007;50(2):151–163
16. Ketterlinus R, Hsieh S-Y, Teng S-H, Lee H, Pusch W. Fishing for biomarkers: analyzing mass spectrometry data with the new clinprotools software. Biotechniques. 2005;38(6):37–40
17. Schleif F-M. Prototype based machine learning for clinical proteomics, Ph.D. thesis. Technical University Clausthal, Technical University Clausthal, Clausthal-Zellerfeld, Germany; 2006.
18. Waagen D, Cassabaum M, Scott C, Schmitt H. Exploring alternative wavelet base selection techniques with application to high resolution radar classification. In: Proceedings of the 6th International Conference on Information Fusion (ISIF’03). Los Alamitos, CA, USA: IEEE Press; 2003;p. 1078–1085
19. Louis ARAK, Maaß P. Wavelets: theory and applications. Wiley; 1998;
20. Leung A, Chau F, Gao J. A review on applications of wavelet transform techniques in chemical analysis: 1989–1997. Chemometrics and Intelligent Laboratory Systems. 1998;43(1):165–184(20)
21. Cohen A, Daubechies I, Feauveau J-C. Biorthogonal bases of compactly supported wavelets. Comm Pure Appl Math. 1992;45(5):485–560
22. Kohonen T. Self-Organizing Maps. Springer Series in Information Sciences, vol. 30. Berlin/Heidelberg: Springer; 1995;(2nd Ext. ed., 1997)
23. Sato A, Yamada K. Generalized learning vector quantization. In:  Touretzky DS,  Mozer MC,  Hasselmo ME editor. Advances in Neural Information Processing Systems 8. Proceedings of the 1995 Conference. Cambridge, MA, USA: MIT Press; 1996;p. 423–429
24. Hammer B, Strickert M, Villmann T. Supervised neural gas with general similarity measure. Neural Processing Letters. 2005;21(1):21–44
25. Villmann T, Hammer B. Supervised neural gas for learning vector quantization. In:  Polani D,  Kim J,  Martinetz T editor. Proceedings of the 5th German Workshop on Artificial Life (GWAL-5). Berlin: Akademische Verlagsgesellschaft-infix-IOS Press; 2002;p. 9–16
26. Martinetz TM, Berkovich SG, Schulten KJ. ‘Neural-gas’ network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks. 1993;4(4):558–569
27. Hammer B, Strickert M, Villmann T. On the generalization ability of GRLVQ networks. Neural Processing Letters. 2005;21(2):109–120
28. Hammer B, Villmann T. Generalized relevance learning vector quantization. Neural Networks. 2002;15(8–9):1059–1068
29. Lee J, Verleysen M. Generalizations of the lp norm for time series and its application to self-organizing maps. In:  Cottrell M editors. Proceedings of the 5th Workshop on Self-Organizing Maps (WSOM) 2005, vol. 1. University Paris-1-Pantheon-Sorbonne on CD-ROM (C) 2005 WSOM’05 Organizing Committee, Paris, France; 2005;p. 733–740
30. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. New York: Springer; 2001;
31. Cordella LP, Foggia P, Sansone C, Tortorella F, Vento M. Reliability parameters to improve combination strategies in multi-expert systems. Pattern Analysis and Applications. 1999;2(3):205–214
32. de Stefano C, Sansone C, Vento M. To reject or not to reject: that is the question: an answer in case of neural classifiers, IEEE Transactions on Systems. Man and Cybernetics Part C. 2000;30(1):84–93
33. Gammerman A, Nouretdinov I, Burford B, Chervonenkis A, Vovk V, Luo Z. Clinical mass spectrometry proteomic diagnosis by conformal predictors, Statistical applications in genetics and molecular biology; 7 (2), article number 13.
34. Shafer G, Vovk V. A tutorial on conformal prediction; 2007. http://alrw.net [accessed 20.10.07].
35. Gammerman A, Vovk V. Prediction algorithms and confidence measures based on algorithmic randomness theory. Theoretical Computer Science. 2002;287:209–217
36. Villmann T, Strickert M, Brüß C, Schleif F-M, Seiffert U. Visualization of fuzzy information in fuzzy-classification for image sagmentation using MDS. In:  Verleysen M editors. Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN) 2007. Evere, Belgium: d-side Publications; 2007;p. 103–108
37. Molinaro A, Simon R, Pfeiffer R. Prediction error estimation: a comparison of resampling methods. Bioinformatics. 2005;21(15):3301–3307
    • MEDLINE
    • CrossRef
38. Villmann T. Sobolev metrics for learning of functional data—mathematical and theoretical aspects, Machine Learning Reports 1 (MLR-03–2007), ISSN:1865-3960. http://www.uni-leipzig.de/∼compint/mlr/mlr_03_2007.pdf [accessed 23.07.08]