High-Dimensional Data Analysis in Cancer Research

Buch | Softcover

392 Seiten

2010 | Softcover reprint of hardcover 1st ed. 2009
Springer-Verlag New York Inc.
978-1-4419-2414-8 (ISBN)

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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.

On the Role and Potential of High-Dimensional Biologic Data in Cancer Research.- Variable selection in regression - estimation, prediction,sparsity, inference.- Multivariate Nonparametric Regression.- Risk Estimation.- Tree-Based Methods.- Support Vector Machine Classification for High Dimensional Microarray Data Analysis, With Applications in Cancer Research.- Bayesian Approaches: Nonparametric Bayesian Analysis of Gene Expression Data.

Erscheint lt. Verlag	19.11.2010
Reihe/Serie	Applied Bioinformatics and Biostatistics in Cancer Research
Zusatzinfo	6 Illustrations, color; 17 Illustrations, black and white; VIII, 392 p. 23 illus., 6 illus. in color.
Verlagsort	New York, NY
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Medizin / Pharmazie ► Medizinische Fachgebiete ► Laboratoriumsmedizin
	Medizin / Pharmazie ► Medizinische Fachgebiete ► Mikrobiologie / Infektologie / Reisemedizin
	Medizin / Pharmazie ► Medizinische Fachgebiete ► Onkologie
	Studium ► 1. Studienabschnitt (Vorklinik) ► Physiologie
	Studium ► 2. Studienabschnitt (Klinik) ► Humangenetik
	Naturwissenschaften ► Biologie ► Zoologie
ISBN-10	1-4419-2414-0 / 1441924140
ISBN-13	978-1-4419-2414-8 / 9781441924148
Zustand	Neuware