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Books > Medicine > Clinical & internal medicine > Diseases & disorders > Oncology

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High-Dimensional Data Analysis in Cancer Research (Hardcover, 1st Edition. 2nd Printing. 2008) Loot Price: R2,784
Discovery Miles 27 840
High-Dimensional Data Analysis in Cancer Research (Hardcover, 1st Edition.
2nd Printing. 2008): Xiaochun Li, Ronghui Xu

High-Dimensional Data Analysis in Cancer Research (Hardcover, 1st Edition. 2nd Printing. 2008)

Xiaochun Li, Ronghui Xu

Series: Applied Bioinformatics and Biostatistics in Cancer Research

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Loot Price R2,784 Discovery Miles 27 840 | Repayment Terms: R261 pm x 12*

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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.

General

Imprint: Springer-Verlag New York
Country of origin: United States
Series: Applied Bioinformatics and Biostatistics in Cancer Research
Release date: December 2008
First published: 2009
Editors: Xiaochun Li • Ronghui Xu
Dimensions: 235 x 155 x 18mm (L x W x T)
Format: Hardcover
Pages: 392
Edition: 1st Edition. 2nd Printing. 2008
ISBN-13: 978-0-387-69763-5
Categories: Books > Medicine > General issues > Medical equipment & techniques > Medical research
Books > Medicine > Clinical & internal medicine > Diseases & disorders > Oncology > General
LSN: 0-387-69763-2
Barcode: 9780387697635

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