Analysis of Microarray Data. A Network–Based Approach

  • ID: 2179769
  • Book
  • 438 Pages
  • John Wiley and Sons Ltd
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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.

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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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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.
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