Analysis of Microarray Data: A Network-Based Approach Edited by Frank Emmert-Streib and Matthias Dehmer Make microarray analysis an even more powerful tool. New Techniques help you top get better results from your experiments. This title is the first to focus on the application of mathematical networks for analyzing microarray data. This method goes well beyond the standard clustering methods traditionally used. From the contents: • Understanding and Preprocessing Microarray Data • Clustering of Microarray Data • Bilayer Verification Algorithm • Probabilistic Boolean Networks as Models for Gene Regulation • Estimating Transcriptional Regulatory Networks by a Bayesian Network • Statistical Methods for Inference of Gen Networks and Regulatory Modules • Graphical Gaussian Models and Relevance Networks • Identification of Genetic Networks by Structural Equations • Network inference by Entropy Maximization • Comparative Network Analysis to Detect Pathological Pathways • Integrating Microarray and ChI P-chip Data • Predicting Functional Modules Using Microarray and Protein Interaction Data The book is for both, scientists using these techniques practically as well as those developing new analysis techniques. Frank Emmert-Streib studied physics at the University of Siegen, Germany, and received his PhD in theoretical Physics from the University of Bremen, Germany. He is currently Senior Fellow at the University of Washington in Seattle, USA, in Biostatistics and Genome Science. Matthias Dehmer studied mathematics at the University of Siegen, Germany, and received his PhD in Computer Science from the Technical University of Darmstadt, Germany. Currently, he holds a research position at Vienna University of Technology, Institute of Discrete Mathematics and Geometry in Vienna, Austria.