| 书目名称 | High-Dimensional Data Analysis in Cancer Research |
| 编辑 | Xiaochun Li,Ronghui Xu |
| 视频video | |
| 概述 | Poses new challenges and calls for scalable solutions to the analysis of such high dimensional data.Present the systematic and analytical approaches and strategies from both biostatistics and bioinfor |
| 丛书名称 | Applied Bioinformatics and Biostatistics in Cancer Research |
| 图书封面 |  |
| 描述 | .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 t |
| 出版日期 | Book 2009 |
| 关键词 | Bayesian Approaches; High-Dimensional Biologic data; Laboratory; Microarray; Multivariate Nonparametric |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-0-387-69765-9 |
| isbn_softcover | 978-1-4419-2414-8 |
| isbn_ebook | 978-0-387-69765-9Series ISSN 2363-9644 Series E-ISSN 2363-9652 |
| issn_series | 2363-9644 |
| copyright | Springer-Verlag New York 2009 |
发表于 2025-3-21 18:16:33
