statistical and machine learning approaches for network analysis, Complete IT Books Pack

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STATISTICAL AND
MACHINE LEARNING
APPROACHES FOR
NETWORK ANALYSIS
STATISTICAL AND
MACHINE LEARNING
APPROACHES FOR
NETWORK ANALYSIS
Edited by
MATTHIAS DEHMER
UMIT – The Health and Life Sciences University, Institute for Bioinformatics and
Translational Research, Hall in Tyrol, Austria
SUBHASH C. BASAK
Natural Resources Research Institute
University of Minnesota, Duluth
Duluth, MN, USA
Copyright © 2012 by John Wiley & Sons, Inc. All rights reserved
Published by John Wiley & Sons, Inc., Hoboken, New Jersey
Published simultaneously in Canada
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Library of Congress Cataloging-in-Publication Data:
ISBN: 978-0-470-19515-4
Printed in the United States of America
10987654321
To Christina
CONTENTS
Preface
ix
Contributors
xi
1 A Survey of Computational Approaches to Reconstruct and
Partition Biological Networks
1
Lipi Acharya, Thair Judeh, and Dongxiao Zhu
2
Introduction to Complex Networks: Measures,
Statistical Properties, and Models
45
Kazuhiro Takemoto and Chikoo Oosawa
3 Modeling for Evolving Biological Networks
77
Kazuhiro Takemoto and Chikoo Oosawa
4 Modularity Configurations in Biological Networks with
Embedded Dynamics
109
Enrico Capobianco, Antonella Travaglione, and Elisabetta Marras
5
Influence of Statistical Estimators on the Large-Scale
Causal Inference of Regulatory Networks
131
Ricardo de Matos Simoes and Frank Emmert-Streib
vii
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