High-Dimensional Data Analysis in Cancer Research

Idioma: inglés

Editorial: Springer New York Nov 2010, 2010

1441924140 / 9781441924148

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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.

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This item is printed on demand - it takes 3-4 days longer - Neuware -Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data. With the advent of high-throughput technologies, various types of high-dimensional data have been generated in recent years for the understanding of biological processes, especially processes that relate to disease occurrence or management of cancer. Motivated by these important applications in cancer research, there has been a dramatic growth in the development of statistical methodology in the analysis of high-dimensional data, particularly related toregression model selection, estimation and prediction.High-Dimensional Data Analysis in Cancer Research, edited by Xiaochun Li and Ronghui Xu, is a collective effort to showcase statistical innovations for meeting the challenges and opportunities uniquely presented by the analytical needs of high-dimensional data in cancer research, particularly in genomics and proteomics. All the chapters included in this volume contain interesting case studies to demonstrate the analysis methodology.High-Dimensional Data Analysis in Cancer Research is an invaluable reference forresearchers, statisticians, bioinformaticians, graduate students and data analysts working in the fields of cancer research. 168 pp. Englisch.

N° de ref. del artículo 9781441924148

Título
High-Dimensional Data Analysis in Cancer Research
Autor
Ronghui Xu
Editorial
Springer New York Nov 2010
Año de publicación
2010
Estado
Neu
Encuadernación
Taschenbuch
Idioma
inglés
ISBN 10
1441924140
ISBN 13
9781441924148
Peso del artículo
265 gramos
Dimensiones
235x155x10 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Alemania

Vendedor de 5 estrellas

Vendedor de AbeBooks desde 11 de enero de 2012

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BuchWeltWeit Ludwig Meier e.K.

Alemania