Computational Biology Issues and Applications in Oncology
Computational Biology: Issues and Applications in Oncology provides a comprehensive report on recent techniques and results in computational oncology essential to the knowledge of scientists, engineers, as well as postgraduate students working on the area
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Series Editors Jeanne Kowalski John Hopkins University, Baltimore, MD, USA Steven Piantadosi Cedars Sinai Medical Center, Los Angeles, CA, USA
For other titles published in this series, go to http://www.springer.com/series/7616
Tuan Pham Editor
Computational Biology Issues and Applications in Oncology
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Editor Tuan Pham Associate Professor School of Engineering and Information Technology The University of New South Wales Canberra, ACT 2600, Australia [email protected]
ISBN 978-1-4419-0810-0 e-ISBN 978-1-4419-0811-7 DOI 10.1007/978-1-4419-0811-7 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2009935696 5 c Springer Science+Business Media, LLC 2009 ° All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business 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 is part of Springer Science+Business Media (www.springer.com)
Preface
Computational biology is an interdisciplinary research that applies approaches and methodologies of information sciences and engineering to address complex problems in biology. With rapid developments in the omics and computer technologies over the past decade, computational biology has been evolving to cover a much wider research domain and applications in order to adequately address challenging problems in systems biology and medicine. This edited book focuses on recent issues and applications of computational biology in oncology. This book contains 11 chapters that cover diverse advanced computational methods applied to oncology in an attempt to find more effective ways for the diagnosis and cure of cancer. Chapter 1 by Chen and Nguyen addresses an analysis of cancer genomics data using partial least squares weights for identifying relevant genes, which are useful for follow-up validations. In Chap. 2, Zhao and Yan report an interesting biclustering method for microarray data analysis, which can handle the case when only a subset of genes coregulates under a subset of conditions and appears to be a novel technique for classifying cancer tissues. As another computational method for microarray data analysis, the work by Lˆe Cao and McLachlan in Chap. 3 discusses the difficulties encountered when dealing with microarray data subjected to selection bias, multiclass, and unbalanced problems, which can be overcome by careful selection of gene expression profiles. Novel methods presented in these chapters c
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