Cancer Informatics by Prototype-networks in Mass Spectrometry

Schleif, Frank-Michael; Villmann, T.; Kostrzewa, M.; Hammer, Barbara; Gammerman, A.

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.

Zeitschriftenaufsatz | Veröffentlicht | Englisch

Download

Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!

DOI

https://doi.org/10.1016/j.artmed.2008.07.018

Autor*in

Schleif, Frank-Michael^UniBi ; Villmann, T.; Kostrzewa, M.; Hammer, Barbara^UniBi ; Gammerman, A.

Einrichtung

Technische Fakultät > AG Machine Learning

Erscheinungsjahr

2009

Zeitschriftentitel

Artificial Intelligence in Medicine

Band

45

Ausgabe

2-3

Seite(n)

215-228

ISSN

0933-3657

Page URI

https://pub.uni-bielefeld.de/record/1993984

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. https://doi.org/10.1016/j.artmed.2008.07.018

Schleif, Frank-Michael, Villmann, T., Kostrzewa, M., Hammer, Barbara, and Gammerman, A. 2009. “Cancer Informatics by Prototype-networks in Mass Spectrometry”. Artificial Intelligence in Medicine 45 (2-3): 215-228.

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.

5 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Recent advances in computational analysis of mass spectrometry for proteomic profiling.
Sun CS, Markey MK., J Mass Spectrom 46(5), 2011
PMID: 21500303

A comparison of methods for classifying clinical samples based on proteomics data: a case study for statistical and machine learning approaches.
Sampson DL, Parker TJ, Upton Z, Hurst CP., PLoS One 6(9), 2011
PMID: 21969867

Mass spectrometry-based clinical proteomics: head-and-neck cancer biomarkers and drug-targets discovery.
Matta A, Ralhan R, DeSouza LV, Siu KW., Mass Spectrom Rev 29(6), 2010
PMID: 20945361

Computational intelligence and machine learning in bioinformatics.
Valentini G, Tagliaferri R, Masulli F., Artif Intell Med 45(2-3), 2009
PMID: 18929473

Swarm intelligence based wavelet coefficient feature selection for mass spectral classification: an application to proteomics data.
Zhao W, Davis CE., Anal Chim Acta 651(1), 2009
PMID: 19733729

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

Optimization and evaluation of surface-enhanced laser-desorption/ionization time-of-flight mass spectrometry for protein profiling of cerebrospinal fluid.
Guerreiro N, Gomez-Mancilla B, Charmont S., Proteome Sci 4(), 2006
PMID: 16643650

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

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