Analysis of Microarray Data is the first to focus on the application of mathematical networks for analyzing microarray data which goes well beyond the standard clustering methods traditionally used. .

Analysis of Microarray Data includes:

• 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

Contents

1. Introduction to DNA Microarrays
2. Comparative Analysis of Clustering Methods for Microarray Data
3. Finding Verified Edges in Genetic/Gene Networks: Bilayer Verification for Network Recovery in the Presence of Hidden Confounders
4. Computational Inference of Biological Causal Networks - Analysis of Therapeutic Compound Effects
5. Reverse Engineering Gene Regulatory Networks with Various Machine Learning Methods
6. Statistical Methods for Inference of Genetic Networks and Regulatory Modules
7. A Model of Genetic Networks with Delayed Stochastic Dynamics
8. Probabilistic Boolean Networks as Models for Gene Regulation
9. Structural Equation for Identification of Genetic Networks
10. Detecting Pathological Pathways of a Complex Disease by a Comparative Analysis of Networks
11. Predicting Functional Modules Using Microarray and Protein Interaction Data
12. Computational Reconstruction of Transcriptional Regulatory Modules of the Yeast Cell Cycle
13. Pathway-Based Methods for Analyzing Microarray Data
14. The Most Probable Genetic Interaction Networks Inferred from Gene Expression Patterns

Index

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