Statistical Diagnostics for Cancer

Analyzing High-Dimensional Data

Nonfiction, Health & Well Being, Medical, Reference, Biostatistics
Cover of the book Statistical Diagnostics for Cancer by Frank Emmert-Streib, Wiley
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Author: Frank Emmert-Streib ISBN: 9783527665457
Publisher: Wiley Publication: November 28, 2012
Imprint: Wiley-Blackwell Language: English
Author: Frank Emmert-Streib
ISBN: 9783527665457
Publisher: Wiley
Publication: November 28, 2012
Imprint: Wiley-Blackwell
Language: English

This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.

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

This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.

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