Cancer Informatics by Prototype-networks in Mass Spectrometry

Schleif F-M, Villmann T, Kostrzewa M, Hammer B, Gammerman A (2009)
Artificial Intelligence in Medicine 45(2-3): 215-228.

Download
Es wurde kein Volltext hochgeladen. Nur Publikationsnachweis!
Zeitschriftenaufsatz | Veröffentlicht | Englisch
Autor
; ; ; ;
Erscheinungsjahr
Zeitschriftentitel
Artificial Intelligence in Medicine
Band
45
Ausgabe
2-3
Seite(n)
215-228
ISSN
PUB-ID

Zitieren

Schleif F-M, Villmann T, Kostrzewa M, Hammer B, Gammerman A. Cancer Informatics by Prototype-networks in Mass Spectrometry. Artificial Intelligence in Medicine. 2009;45(2-3):215-228.
Schleif, F. - M., Villmann, T., Kostrzewa, M., Hammer, B., & Gammerman, A. (2009). Cancer Informatics by Prototype-networks in Mass Spectrometry. Artificial Intelligence in Medicine, 45(2-3), 215-228. doi:10.1016/j.artmed.2008.07.018
Schleif, F. - M., Villmann, T., Kostrzewa, M., Hammer, B., and Gammerman, A. (2009). Cancer Informatics by Prototype-networks in Mass Spectrometry. Artificial Intelligence in Medicine 45, 215-228.
Schleif, F.-M., et al., 2009. Cancer Informatics by Prototype-networks in Mass Spectrometry. Artificial Intelligence in Medicine, 45(2-3), p 215-228.
F.-M. Schleif, et al., “Cancer Informatics by Prototype-networks in Mass Spectrometry”, Artificial Intelligence in Medicine, vol. 45, 2009, pp. 215-228.
Schleif, F.-M., Villmann, T., Kostrzewa, M., Hammer, B., Gammerman, A.: Cancer Informatics by Prototype-networks in Mass Spectrometry. Artificial Intelligence in Medicine. 45, 215-228 (2009).
Schleif, Frank-Michael, Villmann, T., Kostrzewa, M., Hammer, Barbara, and Gammerman, A. “Cancer Informatics by Prototype-networks in Mass Spectrometry”. Artificial Intelligence in Medicine 45.2-3 (2009): 215-228.

38 References

Daten bereitgestellt von Europe PubMed Central.

Mass spectrometry-based clinical proteomics.
Pusch W, Flocco MT, Leung SM, Thiele H, Kostrzewa M., Pharmacogenomics 4(4), 2003
PMID: 12831324
Standardized peptidome profiling of human urine by magnetic bead separation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.
Fiedler GM, Baumann S, Leichtle A, Oltmann A, Kase J, Thiery J, Ceglarek U., Clin. Chem. 53(3), 2007
PMID: 17272489
Magnetic bead based human plasma profiling discriminate acute lymphatic leukaemia from non-diseased samples
Schäffeler, 2004
Salivary protein/peptide profiling with SELDI-TOF-MS.
Schipper R, Loof A, de Groot J, Harthoorn L, van Heerde W, Dransfield E., Ann. N. Y. Acad. Sci. 1098(), 2007
PMID: 17435159

Bishop, 2006
Supervised neural gas with general similarity measure
Hammer, Neural Processing Letters 21(1), 2005
Supervised neural gas and relevance learning in learning vector quantisation
Villmann, 2003
Supervised relevance neural gas and unified maximum separability analysis for classification of mass spectrometric data
Schleif, 2004
Comparison of relevance learning vector quantization with other metric adaptive classification methods
Villmann, Neural Networks 19(15), 2005
Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods.
Villmann T, Schleif FM, Kostrzewa M, Walch A, Hammer B., Brief. Bioinformatics 9(2), 2008
PMID: 18334515
Analysis of proteomic spectral data by multi resolution analysis and self-organizing-maps
Schleif, 2007
Supervised neural gas for functional data and its application to the analysis of clinical proteom spectra
Schleif, 2007

Vovk, 2005
Hedging predictions in machine learning
Gammerman, The Computer Journal 50(2), 2007
Fishing for biomarkers: analyzing mass spectrometry data with the new clinprotools software
Ketterlinus, Biotechniques 38(6), 2005

AUTHOR UNKNOWN, 0
Exploring alternative wavelet base selection techniques with application to high resolution radar classification
Waagen, 2003

Louis, 1998
A review on applications of wavelet transform techniques in chemical analysis: 1989–1997
Leung, Chemometrics and Intelligent Laboratory Systems 43(1), 1998
Biorthogonal bases of compactly supported wavelets
Cohen, Comm Pure Appl Math 45(5), 1992

Kohonen, 1995
Generalized learning vector quantization
Sato, 1996
Supervised neural gas with general similarity measure
Hammer, Neural Processing Letters 21(1), 2005
Supervised neural gas for learning vector quantization
Villmann, 2002
;Neural-gas' network for vector quantization and its application to time-series prediction.
Martinetz TM, Berkovich SG, Schulten KJ., IEEE Trans Neural Netw 4(4), 1993
PMID: 18267757
On the generalization ability of GRLVQ networks
Hammer, Neural Processing Letters 21(2), 2005
Generalized relevance learning vector quantization.
Hammer B, Villmann T., Neural Netw 15(8-9), 2002
PMID: 12416694
Generalizations of the lp norm for time series and its application to self-organizing maps
Lee, 2005

Hastie, 2001
Reliability parameters to improve combination strategies in multi-expert systems
Cordella, Pattern Analysis and Applications 2(3), 1999
To reject or not to reject: that is the question: an answer in case of neural classifiers, IEEE Transactions on Systems
de, Man and Cybernetics Part C 30(1), 2000

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Prediction algorithms and confidence measures based on algorithmic randomness theory
Gammerman, Theoretical Computer Science 287(), 2002
Visualization of fuzzy information in fuzzy-classification for image sagmentation using MDS
Villmann, 2007
Prediction error estimation: a comparison of resampling methods.
Molinaro AM, Simon R, Pfeiffer RM., Bioinformatics 21(15), 2005
PMID: 15905277

AUTHOR UNKNOWN, 0

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®

Quellen

PMID: 18778925
PubMed | Europe PMC

Suchen in

Google Scholar