Stock image for illustration purposes only - book cover, edition or condition may vary.
High-Dimensional Data Analysis in Cancer Research: Approaches to the Analysis of High-dimensional Data in Oncology (Applied Bioinformatics and Biostatistics in Cancer Research)
Xiaochun Li (Ed.)
€ 123.08
FREE Delivery in Ireland
Description for High-Dimensional Data Analysis in Cancer Research: Approaches to the Analysis of High-dimensional Data in Oncology (Applied Bioinformatics and Biostatistics in Cancer Research)
Hardcover. This volume presents the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data. It poses new challenges and calls for scalable solutions. Editor(s): Li, Xiaochun; Xu, Ronghui. Series: Applied Bioinformatics and Biostatistics in Cancer Research. Num Pages: 392 pages, 17 black & white illustrations, 6 colour illustrations, 10 black & white tables, biograph. BIC Classification: MBGR; MJCL. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 11. Weight in Grams: 421.
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 ... Read more
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 ... Read more
Product Details
Condition
New
Publisher
Springer
Format
Hardback
Publication date
2009
Series
Applied Bioinformatics and Biostatistics in Cancer Research
Weight
""
Dustjacket Condition
""
Number of Pages
392
Place of Publication
New York, NY, United States
ISBN
9780387697635
SKU
V9780387697635
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
Reviews for High-Dimensional Data Analysis in Cancer Research: Approaches to the Analysis of High-dimensional Data in Oncology (Applied Bioinformatics and Biostatistics in Cancer Research)