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ORCIDDagstuhl Seminar Proceedings: Computational Proteomics 2005
- Christian G. Huber, Oliver Kohlbacher, Knut Reinert:
Computational Proteomics, 20.-25. November 2005. Dagstuhl Seminar Proceedings 05471, Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany 2006 - Clemens Gröpl:
An Algorithm for Feature Finding in LC/MS Raw Data. - Eva Lange, Clemens Gröpl, Oliver Kohlbacher, Andreas Hildebrandt:
High-accuracy peak picking of proteomics data. - Andreas Tholey:
MALDI Mass Spectrometry for Quantitative Proteomics - Approaches, Scopes and Limitations. - Ole Schulz-Trieglaff:
Software platforms for quantitative proteomics. - Christian Schley, Matthias Altmeyer, Rolf Müller, Christian G. Huber:
Multidimensional Peptide/Protein Analysis and Identification by Sequence Database Search Using Mass Spectrometric Data. - Christian G. Huber, Oliver Kohlbacher, Knut Reinert:
05471 Executive Summary - Computational Proteomics. - Nathanaël Delmotte, Bettina M. Mayr, Andreas Leinenbach, Knut Reinert, Oliver Kohlbacher, Christoph L. Klein, Christian G. Huber:
Evaluation of LC-MS data for the absolute quantitative analysis of marker proteins. - Tim Conrad:
New statistical algorithms for clinical proteomics. - Hansjörg Toll, Peter Berger, Andreas Hofmann, Andreas Hildebrandt, Herbert Oberacher, Hans-Peter Lenhof, Christian G. Huber:
Glycosylation Patterns of Proteins Studied by Liquid Chromatography-Mass Spectrometry and Bioinformatic Tools. - Hartmut Schlüter:
Future Challenges in Proteomics. - Sebastian Böcker, Veli Mäkinen:
Combinatorial Approaches for Mass Spectra Recalibration. - Wolf D. Lehmann, Junhua Wei, Juri Rappsilber, Mojiborahman Salek:
The Peptide MS/MS-Fragmentome: A Set of Predictable Fragment Ions with Highly Redundant Sequence Information. - Marc Sturm, Sascha Quinten, Christian G. Huber, Oliver Kohlbacher:
A machine learning approach for prediction of DNA and peptide retention times. - Christian G. Huber, Oliver Kohlbacher, Knut Reinert:
05471 Abstract Collection - Computational Proteomics.
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