Data Mining Techniques for the Life Sciences
Whereas getting exact data about living systems and sophisticated experimental procedures have primarily absorbed the minds of researchers previously, the development of high-throughput technologies has caused the weight to increasingly shift to the probl
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M O L E C U L A R B I O L O G Y TM
Series Editor John M. Walker School of Life Sciences University of Hertfordshire Hatfield, Hertfordshire, AL10 9AB, UK
For other titles published in this series, go to www.springer.com/series/7651
Data Mining Techniques for the Life Sciences Edited by
Oliviero Carugo University of Pavia, Pavia, Italy Vienna University, Vienna, Austria
Frank Eisenhaber Bioinformatics Institute, Agency for Science, Technology and Research, Singapore
Editors Oliviero Carugo Universita¨t Wien Max F. Perutz Laboratories GmbH Structural & Computational Biology Group Dr. Bohr-Gasse 9 1030 Wien Campus-Vienna-Biocenter Austria [email protected]
Frank Eisenhaber Bioinformatics Institute (BII) Agency for Science, Technology and Research (A*STAR) 30 Biopolis Street, Singapore 138671 #07-01 Matrix Building Singapore [email protected]
ISSN 1064-3745 e-ISSN 1940-6029 ISBN 978-1-60327-240-7 e-ISBN 978-1-60327-241-4 DOI 10.1007/978-1-60327-241-4 Library of Congress Control Number: 2009939505 # Humana Press, a part of Springer ScienceþBusiness Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Humana Press, c/o Springer ScienceþBusiness Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper springer.com
Preface Most life science researchers will agree that biology is not a truly theoretical branch of science. The hype around computational biology and bioinformatics beginning in the nineties of the 20th century was to be short lived (1, 2). When almost no value of practical importance such as the optimal dose of a drug or the three-dimensional structure of an orphan protein can be computed from fundamental principles, it is still more straightforward to determine them experimentally. Thus, experiments and observations do generate the overwhelming part of insights into biology and medicine. The extrapolation depth and the prediction power of the theoretical argument in life sciences still have a long way to go. Yet, two trends have qualitatively changed the way how biological research is done today. The number of researchers has dramatically grown and they, armed with the same protocols, have produced lots of similarly structured data. Finally, high-throughput technologies such as DNA sequencing or array-based expression profiling have been around for just a decade. Nevertheless, with their high level of uniform data generation, they reach the threshold of
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