High-Dimensional Data Analysis in Cancer Research

✍ Scribed by Ross L. Prentice (auth.), Xiaochun Li, Ronghui Xu (eds.)

Publisher: Springer-Verlag New York
Year: 2009
Tongue: English
Leaves: 164
Series: Applied Bioinformatics and Biostatistics in Cancer Research
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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 cancer research, there has been a dramatic growth in the development of statistical methodology in the analysis of high-dimensional data, particularly related to
regression model selection, estimation and prediction.

High-Dimensional Data Analysis in Cancer Research, edited by Xiaochun Li and Ronghui Xu, is a collective effort to showcase statistical innovations for meeting the challenges and opportunities uniquely presented by the analytical needs of high-dimensional data in cancer research, particularly in genomics and proteomics. All the chapters included in this volume contain interesting case studies to demonstrate the analysis methodology.

High-Dimensional Data Analysis in Cancer Research is an invaluable reference for
researchers, statisticians, bioinformaticians, graduate students and data analysts working in the fields of cancer research.

✦ Table of Contents

Front Matter....Pages 1-13
On the Role and Potential of High-Dimensional Biologic Data in Cancer Research....Pages 1-11
Variable selection in regression - estimation, prediction,sparsity, inference....Pages 1-21
Multivariate Nonparametric Regression....Pages 1-24
Risk Estimation....Pages 1-23
Tree-Based Methods....Pages 1-19
Support Vector Machine Classification for High Dimensional Microarray Data Analysis, With Applications in Cancer Research....Pages 1-24
Bayesian Approaches: Nonparametric Bayesian Analysis of Gene Expression Data....Pages 1-20
Back Matter....Pages 1-8

✦ Subjects

Cancer Research; Laboratory Medicine; Human Genetics; Medical Microbiology; Molecular Medicine; Neurosciences

📜 SIMILAR VOLUMES

High-Dimensional Data Analysis in Cancer

📁 High-Dimensional Data Analysis in Cancer Research

✍ Ross L. Prentice (auth.), Xiaochun Li, Ronghui Xu (eds.) 📂 Library 📅 2009 🏛 Springer-Verlag New York 🌐 English

<p><P>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 i

High-dimensional Data Analysis

📁 High-dimensional Data Analysis

✍ T Tony Cai; Xiaotong Shen (eds.) 📂 Library 📅 2010 🏛 WS 🌐 English

Ordinary differential equations (ODEs), differential-algebraic equations (DAEs) and partial differential equations (PDEs) are among the forms of mathematics most widely used in science and engineering. Each of these equation types is a focal point for international collaboration and research. This b

High-Dimensional Data Analysis with Low-

📁 High-Dimensional Data Analysis with Low-Dimensional Models

✍ John Wright, Yi Ma 📂 Library 📅 2022 🏛 Cambridge University Press 🌐 English

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional model

High-dimensional Microarray Data Analysi

📁 High-dimensional Microarray Data Analysis: Cancer Gene Diagnosis and Malignancy Indexes by Microarray

✍ Shuichi Shinmura 📂 Library 📅 2019 🏛 Springer Singapore 🌐 English

<p>This book shows how to decompose high-dimensional microarrays into small subspaces (Small Matryoshkas, SMs), statistically analyze them, and perform cancer gene diagnosis. The information is useful for genetic experts, anyone who analyzes genetic data, and students to use as practical textbooks.<

Statistical Diagnostics for Cancer: Anal

📁 Statistical Diagnostics for Cancer: Analyzing High-Dimensional Data

✍ M. Dehmer, Frank Emmert?Streib(eds.) 📂 Library 🌐 English

This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or les

Analysis of Multivariate and High-Dimens

📁 Analysis of Multivariate and High-Dimensional Data

✍ Koch I. 📂 Library 📅 2013 🏛 Cambridge University Press 🌐 English

Big data poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical learning. Th