This book 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* Reconstruction of the Yeast Cell Cycle by Partial Correlations of Higher Order* Bilayer Verification Algorithm* Probabilistic Boolean Networks as Models for Gene Regulation* Estimating Transcriptional Regulatory Networks by a Bayesian Network* Analysis of Therapeutic Compound Effects* Statistical Methods for Inference of Genetic Networks and Regulatory Modules* Identification of Genetic Networks by Structural Equations* Predicting Functional Modules Using Microarray and Protein Interaction Data* Integrating Results from Literature Mining and Microarray Experiments to Infer Gene Networks The book is for both, scientists using the technique 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 Sciences. 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.
Introduction to DNA Microarrays Comparative Analysis of Clustering Methods for Microarray Data Finding Verified Edges in Genetic/Gene Networks: Bilayer Verification for Network Recovery in the Presence Computational Inference of Biological Causal Networks - Analysis of Therapeutic Compound Effects Reverse Engineering Gene Regulatory Networks with Various Machine Learning Methods Statistical Methods for Inference of Genetic Networks and Regulatory Modules A Model of Genetic Networks with Delayed Stochastic Dynamics Probabilistic Boolean Networks as Models for Gene Regulation Structural Equation for Identification of Genetic Networks Detecting Pathological Pathways of a Complex Disease by a Comparative Analysis of Networks Predicting Functional Modules Using Microarray and Protein Interaction Data Computational Reconstruction of Transcriptional Regulatory Modules of the Yeast Cell Cycle Pathway-Based Methods for Analyzing Microarray Data The Most Probable Genetic Interaction Networks Inferred from Gene Expression Patterns
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