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.
Table des matières
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
A propos de l’auteur
Frank Emmert-Streib studied physics at the University of Siegen, Germany, and received his Ph D 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 Ph D 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.
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