Medical Biostatistics for Complex Diseases

Eine Sammlung von Methoden und Hilfsmitteln zur Analyse von Daten, die in Hochdurchsatz-Studien der wichtigsten Krankheiten der westlichen Industrieländer (u.a. Krebs, Herz- und Gefäßkrankheiten) gewonnen wurden. In allen Fällen spielen mehrere Faktoren und große Datensätze die beherrschende Rolle bei der Wahl der Algorithmus. Die meisten Beiträge der Monographie stammen von Mathematikern. Interessant ist das Buch vorrangig für Forscher und Kliniker, die mit der Auswertung des Datenmaterials befasst sind, denen aber ein tiefer gehender Hintergrund in Mathematik und Statistik fehlt.

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.