Medical Biostatistics for Complex Diseases

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  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2010-06-08
  • Publisher: Wiley-Blackwell

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The book presents a collection of highly valuable tools which allow the development of powerful methods to analyze high-throughput data derived from studies from complex diseases. These diseases comprise the major health challenges in industrialized countries and include cancer and cardiovascular disease. They have in common that they are multi-factored and that the analyses of large data sets are therefore particular challenging. Here, a global team of high profile authors put together an overview of current approaches to analyze these data. Written mostly by mathematicians, this book is a valuable resource for the large number of researchers and clinicians who have to deal with these data sets but do not have the appropriate background in statistics and mathemetics.

Author Biography

Frank Emmert-Streib studied physics at the University of Siegen, Germany, and received his PhD in theoretical physics from the University of Bremen. He was a postdoctoral research associate in the department for Bioinformatics at the Stowers Institute for Medical Research in Kansas City, USA, and a senior fellow in the departments of Biostatistics and Genome Sciences at the University of Washington, Seattle, USA. Currently he is an assistant professor at the Queen's University Belfast at the Center for Cancer Research and Cell Biology, leading the Computational Biology and Machine Learning group. Frank Emmert-Streib's research interests are in the field of computational biology, biostatistics, network biology and machine learning, focusing on the development and application of methods to analyze high-dimensional, large-scale data from molecular biology.
Matthias Dehmer studied mathematics at the University of Siegen, Germany and received his PhD in computer science from the Darmstadt University of Technology. Following this, he was a research fellow at the Vienna Bio Center, Austria, and at the Vienna University of Technology. He is currently an associate professor at UMIT - The Health and Life Sciences University in Hall in Tirol, Austria. His research interests are in bioinformatics, systems biology, complex networks, statistics and information theory. In particular, Matthias Dehmer is working on machine learning-based methods to design new data analysis methods for solving problems in computational and systems biology.

Table of Contents

General Biological And Statistical Basics
The biology of MYC in health and disease: a high altitude view
Cancer Stem Cells ? Finding and Hitting the Roots of Cancer
Multiple Testing Methods
Statistical And Computational Analysis Methods
Making Mountains Out of Molehills: Moving from Single Gene to Pathway Based Models of Colon Cancer Progression
Gene-Set Expression Analysis: Challenges and Tools
Hotelling?s T-2 multivariate profiling for detecting differential expression in microarrays
Interpreting differential coexpression of gene sets
Multivariate analysis of microarray data: Application of MANOVA
Testing Significance of a Class of Genes
Differential dependency network analysis to identify topological changes in biological networks
An Introduction to Time-Varying Connectivity Estimation for Gene Regulatory Networks
A systems biology approach to construct a cancer-perturbed protein-protein interaction network for apoptosis by means of microarray and database mining
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Kernel Classification Methods for Cancer Microarray Data
Predicting Cancer Survival Using Expression Patterns
Integration of microarray data sets
Model Averaging For Biological Networks With Prior Information
Table of Contents provided by Publisher. All Rights Reserved.

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