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Item Details
Title: APPLIED STATISTICS FOR NETWORK BIOLOGY
METHODS IN SYSTEMS BIOLOGY
By: Matthias Dehmer (Editor), Frank Emmert-Streib (Editor), Armin Graber (Editor)
Format: Hardback

List price: £129.95


We currently do not stock this item, please contact the publisher directly for further information.

ISBN 10: 3527327509
ISBN 13: 9783527327508
Publisher: WILEY-VCH VERLAG GMBH
Pub. date: 20 April, 2011
Series: Quantitative and Network Biology (VCH)
Pages: 478
Description: The book introduces to the reader a number of cutting edge statistical methods which can e used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze as many data as possible in a given biological system (such as a cell or an organ).
Synopsis: The book introduces to the reader a number of cutting edge statistical methods which can e used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze as many data as possible in a given biological system (such as a cell or an organ). The appropriate statistical evaluation of these large scale data is critical for the correct interpretation and different experimental approaches require different approaches for the statistical analysis of these data. This book is written by biostatisticians and mathematicians but aimed as a valuable guide for the experimental researcher as well computational biologists who often lack an appropriate background in statistical analysis.
Illustrations: Illustrations, maps
Publication: Germany
Imprint: Wiley-VCH Verlag GmbH
Returns: Returnable
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COMPUTATIONAL NETWORK ANALYSIS WITH R
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COMPUTATIONAL NETWORK THEORY
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ELEMENTS OF DATA SCIENCE, MACHINE LEARNING, AND ARTIFICIAL INTELLIGENCE USING R (HB)
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GRAPH POLYNOMIALS
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INFORMATION THEORY AND STATISTICAL LEARNING (HB)
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MATHEMATICAL FOUNDATIONS AND APPLICATIONS OF GRAPH ENTROPY
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MEDICAL BIOSTATISTICS FOR COMPLEX DISEASES
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QUANTITATIVE GRAPH THEORY
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STATISTICAL AND MACHINE LEARNING APPROACHES FOR NETWORK ANALYSIS
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STATISTICAL DIAGNOSTICS FOR CANCER
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STATISTICAL MODELLING OF MOLECULAR DESCRIPTORS IN QSAR/QSPR
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