Pattern Recognition in Bioinformatics 6th IAPR International Con
This book constitutes the refereed proceedings of the 6th International Conference on Pattern Recognition in Bioinformatics, PRIB 2011, held in Delft, The Netherlands, in November 2011. The 29 revised full papers presented were carefully reviewed and sele
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Edited by S. Istrail, P. Pevzner, and M. Waterman Editorial Board: A. Apostolico S. Brunak M. Gelfand T. Lengauer S. Miyano G. Myers M.-F. Sagot D. Sankoff R. Shamir T. Speed M. Vingron W. Wong
Subseries of Lecture Notes in Computer Science
Marco Loog Lodewyk Wessels Marcel J.T. Reinders Dick de Ridder (Eds.)
Pattern Recognition in Bioinformatics 6th IAPR International Conference, PRIB 2011 Delft, The Netherlands, November 2-4, 2011 Proceedings
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Series Editors Sorin Istrail, Brown University, Providence, RI, USA Pavel Pevzner, University of California, San Diego, CA, USA Michael Waterman, University of Southern California, Los Angeles, CA, USA Volume Editors Marco Loog Marcel J.T. Reinders Dick de Ridder Delft University of Technology Pattern Recognition & Bioinformatics Group Mekelweg 4, 2628 CD Delft, The Netherlands E-mail: {m.loog, m.j.t.reinders, d.deridder}@tudelft.nl Lodewyk Wessels Netherlands Cancer Institute Bioinformatics and Statistics Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands E-mail: [email protected]
ISSN 0302-9743 e-ISSN 1611-3349 ISBN 978-3-642-24854-2 e-ISBN 978-3-642-24855-9 DOI 10.1007/978-3-642-24855-9 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011939081 CR Subject Classification (1998): J.3, I.5, F.2.2, I.2, I.4, H.3.3, H.2.8 LNCS Sublibrary: SL 8 – Bioinformatics
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Preface
In modern biology, high-throughput measurement devices allow life scientists to gather data at unprecedented rates. To make sense of these data, computational biologists and system biologists construct quantitative models, many of which depend on pattern recognition techniques. Application of these techniques is challenging due to the large volumes of data and background information, noisy measurements and target outputs, highly diverse data types etc. To address these bioinformatics challenges, research in pattern recognition has been forced to move beyond the simple application of c
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