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170503s2019||||enk o ||1 0|eng|d
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9781108377706 (ebook)
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9781108432238 (paperback)
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(UkCbUP)CR9781108377706
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UkCbUP
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eng
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rda
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UkCbUP
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R858
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.A469 2019
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Analyzing network data in biology and medicine :
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an interdisciplinary textbook for biological, medical and computational scientists /
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edited and authored by Nataša Pržulj.
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Cambridge :
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Cambridge University Press,
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2019.
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1 online resource (xiv, 632 pages) :
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digital, PDF file(s).
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text
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txt
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rdacontent
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computer
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rdamedia
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online resource
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rdacarrier
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Title from publisher's bibliographic system (viewed on 13 Mar 2019).
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Access restricted by licensing agreement.
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The increased and widespread availability of large network data resources in recent years has resulted in a growing need for effective methods for their analysis. The challenge is to detect patterns that provide a better understanding of the data. However, this is not a straight forward task because of the size of the data sets and the computer power required for the analysis. The solution is to devise methods for approximately answering the questions posed, and these methods will vary depending on the data sets under scrutiny. This cutting-edge text introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical data sets, using a wide range of real-life examples. Bringing together leading experts, this text provides an ideal introduction to and insight into the interdisciplinary field of network data analysis in biomedicine.
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Access is available to the Yale community.
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0
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Medical informatics
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Data processing.
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Bioinformatics.
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Pržulj, Nataša,
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editor.
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Cambridge University Press eBook Backlist 2018-2019.
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8
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Print version:
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9781108432238
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yulint
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None
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Online resource
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0
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Online resource
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Online book
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https://yale.idm.oclc.org/login?URL=https://doi.org/10.1017/9781108377706
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R858
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Yale Internet Resource
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Yale Internet Resource >> None|DELIM|15351611
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online resource
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2020-06-05T14:09:03.000Z
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DO NOT EDIT. DO NOT EXPORT.
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0
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https://doi.org/10.1017/9781108377706