Correlation-based network analysis of cancer metabolism

A new systems biology approach in metabolomics

Nonfiction, Science & Nature, Science, Other Sciences, Molecular Biology, Biological Sciences
Cover of the book Correlation-based network analysis of cancer metabolism by Helen L. Kotze, Kaye J. Williams, Emily G. Armitage, Springer New York
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Author: Helen L. Kotze, Kaye J. Williams, Emily G. Armitage ISBN: 9781493906154
Publisher: Springer New York Publication: May 12, 2014
Imprint: Springer Language: English
Author: Helen L. Kotze, Kaye J. Williams, Emily G. Armitage
ISBN: 9781493906154
Publisher: Springer New York
Publication: May 12, 2014
Imprint: Springer
Language: English

With the rise of systems biology as an approach in biochemistry research, using high throughput techniques such as mass spectrometry to generate metabolic profiles of cancer metabolism is becoming increasingly popular. There are examples of cancer metabolic profiling studies in the academic literature; however they are often only in journals specific to the metabolomics community. This book will be particularly useful for post-graduate students and post-doctoral researchers using this pioneering technique of network-based correlation analysis. The approach can be adapted to the analysis of any large scale metabolic profiling experiment to answer a range of biological questions in a range of species or for a range of diseases.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

With the rise of systems biology as an approach in biochemistry research, using high throughput techniques such as mass spectrometry to generate metabolic profiles of cancer metabolism is becoming increasingly popular. There are examples of cancer metabolic profiling studies in the academic literature; however they are often only in journals specific to the metabolomics community. This book will be particularly useful for post-graduate students and post-doctoral researchers using this pioneering technique of network-based correlation analysis. The approach can be adapted to the analysis of any large scale metabolic profiling experiment to answer a range of biological questions in a range of species or for a range of diseases.

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