High-dimensional Data Analysis in Cancer Research: Approaches to the Analysis of High-dimensional Data in Oncology (Applied Bioinformatics and Biostatistics in Cancer Research)
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1
High-Dimensional Data Analysis in Cancer Research (2009)
EN NWISBN: 9780387697635 bzw. 0387697632, in Englisch, Springer-Verlag Gmbh Jan 2009, neu.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, NDS, Germany.
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. 149 pp. Englisch.
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. 149 pp. Englisch.
2
High-Dimensional Data Analysis in Cancer Research (Applied Bioinformatics and Biostatistics in Cancer Research) (2009)
EN HC NW FEISBN: 9780387697635 bzw. 0387697632, in Englisch, 392 Seiten, Springer, gebundenes Buch, neu, Erstausgabe.
Lieferung aus: Vereinigte Staaten von Amerika, Usually ships in 1-2 business days.
Von Händler/Antiquariat, Vibgyor Books.
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., Hardcover, Ausgabe: 1st Edition. 2nd Printing. 2008, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2009-01-16, Studio: Springer, Verkaufsrang: 1041418.
Von Händler/Antiquariat, Vibgyor Books.
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., Hardcover, Ausgabe: 1st Edition. 2nd Printing. 2008, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2009-01-16, Studio: Springer, Verkaufsrang: 1041418.
3
HIGH-DIMENSIONAL DATA ANALYSIS IN CANCER RESEARCH
EN NWISBN: 9780387697635 bzw. 0387697632, in Englisch, Springer, Deutschland, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, TEXTBOOKSZONE [56595990], NEW DELHI, India.
This is an Original US Edition, With ShrinkWrap. Perfect condition.Please contact us for any questions regarding this book.!!
Von Händler/Antiquariat, TEXTBOOKSZONE [56595990], NEW DELHI, India.
This is an Original US Edition, With ShrinkWrap. Perfect condition.Please contact us for any questions regarding this book.!!
4
High-Dimensional Data Analysis in Cancer Research (Applied Bioinformatics and Biostatistics in Cancer Research)
EN HC NWISBN: 9780387697635 bzw. 0387697632, in Englisch, Springer, gebundenes Buch, neu.
Von Händler/Antiquariat, ExtremelyReliable [8304062], Richmond, TX, U.S.A.
This item is printed on demand.
This item is printed on demand.
5
High-Dimensional Data Analysis in Oncology (2010)
EN NWISBN: 9780387697635 bzw. 0387697632, in Englisch, Springer, Deutschland, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, EBOOKSTORE2010 [55268631], NajafGarh, ND, India.
New. US edition. Customer Satisfaction guaranteed!!
Von Händler/Antiquariat, EBOOKSTORE2010 [55268631], NajafGarh, ND, India.
New. US edition. Customer Satisfaction guaranteed!!
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