Medical Biostatistics for Complex Diseases

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.

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.