Data Mining Techniques for the Life Sciences
High throughput sequencing (HTS) technologies have conquered the genomics and epigenomics worlds. The applications of HTS methods are wide, and can be used to sequence everything from whole or partial genomes, transcriptomes, non-coding RNAs, ribosome pro
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Oliviero Carugo Frank Eisenhaber Editors
Data Mining Techniques for the Life Sciences Second Edition
METHODS
IN
MOLECULAR BIOLOGY
Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, AL10 9AB, UK
For further volumes: http://www.springer.com/series/7651
Data Mining Techniques for the Life Sciences Edited by
Oliviero Carugo Department of Chemistry, University of Pavia, Pavia, Italy; Department of Structural and Computational Biology, MFPL—Vienna University, Campus Vienna, Vienna, Austria
Frank Eisenhaber Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
Editors Oliviero Carugo Department of Chemistry University of Pavia, Pavia, Italy Department of Structural and Computational Biology MFPL—Vienna University, Campus Vienna Vienna, Austria
Frank Eisenhaber Bioinformatics Institute (BII), Agency for Science Technology and Research (A*STAR) Singapore, Singapore
ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-4939-3570-3 ISBN 978-1-4939-3572-7 (eBook) DOI 10.1007/978-1-4939-3572-7 Library of Congress Control Number: 2016935704 © Springer Science+Business Media New York 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Humana Press imprint is published by Springer Nature The registered company is Springer Science+Business Media LLC New York
Preface The new edition of this book is rather different from the first edition, though the general organization may seem quite similar. A new, small part, focused on the Big Data issue, has been added to the three parts already present in the first edition (Databases, Computational Techniques, and Prediction Methods). And the contents of the old parts have been substantially modified. The book philosophy was maintained. Since the theoretical foundations of the biological sciences are extremely feeble, any discovery must be strictly empirical
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