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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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ISBN 10: 1108428878 ISBN 13: 9781108428873
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ISBN 10: 1108428878 ISBN 13: 9781108428873
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ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, 2023
ISBN 10: 1108497667 ISBN 13: 9781108497664
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Published by Cambridge University Press, 2023
ISBN 10: 1108497667 ISBN 13: 9781108497664
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Published by Cambridge University Press, Cambridge, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Hardcover. Condition: new. Hardcover. Biological systems are extremely complex and have emergent properties that cannot be explained or even predicted by studying their individual parts in isolation. The reductionist approach, although successful in the early days of molecular biology, underestimates this complexity. As the amount of available data grows, so it will become increasingly important to be able to analyse and integrate these large data sets. This book introduces novel approaches and solutions to the Big Data problem in biomedicine, and presents new techniques in the field of graph theory for handling and processing multi-type large data sets. By discussing cutting-edge problems and techniques, researchers from a wide range of fields will be able to gain insights for exploiting big heterogonous data in the life sciences through the concept of 'network of networks'. Introduces new graph theory techniques for the analysis and integration of multi-type large data sets) in the life sciences. Discussing cutting-edge problems and techniques, this book provides researchers from a wide range of fields with methods for exploiting big heterogeneous data in biology through the concept of 'network of networks'. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, GB, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Hardback. Condition: New. Biological systems are extremely complex and have emergent properties that cannot be explained or even predicted by studying their individual parts in isolation. The reductionist approach, although successful in the early days of molecular biology, underestimates this complexity. As the amount of available data grows, so it will become increasingly important to be able to analyse and integrate these large data sets. This book introduces novel approaches and solutions to the Big Data problem in biomedicine, and presents new techniques in the field of graph theory for handling and processing multi-type large data sets. By discussing cutting-edge problems and techniques, researchers from a wide range of fields will be able to gain insights for exploiting big heterogonous data in the life sciences through the concept of 'network of networks'.
Language: English
Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press, 2021
ISBN 10: 1108428878 ISBN 13: 9781108428873
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Published by Cambridge University Press 2023-05-25, 2023
ISBN 10: 1108497667 ISBN 13: 9781108497664
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Published by Cambridge University Press, 2023
ISBN 10: 1108497667 ISBN 13: 9781108497664
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Language: English
Published by Cambridge University Press, 2023
ISBN 10: 1108497667 ISBN 13: 9781108497664
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Condition: New.