Analyzing Network Data in Biology and Medicine: An Interdisciplinary Textbook for Biological, Medical and Computational Scientists Contributor(s): Przulj, Natasa (Editor) |
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ISBN: 1108432239 ISBN-13: 9781108432238 Publisher: Cambridge University Press
Binding Type: Paperback - See All Available Formats & Editions Published: March 2019 |
Additional Information |
BISAC Categories: - Science | Life Sciences - Genetics & Genomics - Medical | Family & General Practice |
Dewey: 610.285 |
LCCN: 2018034214 |
Physical Information: 1" H x 8.4" W x 9.8" L (2.80 lbs) 643 pages |
Features: Bibliography, Price on Product |
Descriptions, Reviews, Etc. |
Publisher Description: 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 straightforward 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. |
Contributor Bio(s): Przulj, Natasa: - Natasa Przulj is Professor of Biomedical Data Science at University College London and an ICREA Research Professor at Barcelona Supercomputing Center. She has been an elected academician of The Academy of Europe, Academia Europaea, since 2017 and is a Fellow of the British Computer Society (BCS). She is recognized for designing methods to mine large real-world molecular network datasets and for extending and using machine learning methods for the integration of heterogeneous biomedical and molecular data, applied to advancing biological and medical knowledge. She received two prestigious European Research Council (ERC) research grants, Starting (2012-17) and Consolidator (2018-23), as well as USA National Science Foundation (NSF) grants, among others. She is a recipient of the BCS Roger Needham Award for 2014. |
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