High-Dimensional Data Analysis in Cancer Research

• Corporate Author: SpringerLink (Online service)
• Other Authors: Li, Xiaochun (Editor), Xu, Ronghui (Editor)
• Format: Electronic eBook
• Language: English
• Published: New York, NY : Springer New York, 2009.
• Series: Applied Bioinformatics and Biostatistics in Cancer Research
• Subjects: Medicine.; Cancer research.; Human genetics.; Laboratory medicine.; Medical microbiology.; Molecular biology.; Neurosciences.; Biomedicine.; Cancer Research.; Laboratory Medicine.; Human Genetics.; Medical Microbiology.; Molecular Medicine.

With the advent of high-throughput technologies, various types of high-dimensional data have been generated in recent years for the understanding of biological processes, especially processes that relate to disease occurrence or management of cancer. Motivated by these important applications in canc...

Table of Contents:

• 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.