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Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics

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Cover of 'Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics'

Table of Contents

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    Book Overview
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    Chapter 1 Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics
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    Chapter 2 Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs
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    Chapter 3 Hypothesis Testing with Classifier Systems for Rule-Based Risk Prediction
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    Chapter 4 Robust Peak Detection and Alignment of nanoLC-FT Mass Spectrometry Data
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    Chapter 5 One-Versus-One and One-Versus-All Multiclass SVM-RFE for Gene Selection in Cancer Classification
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    Chapter 6 Understanding Signal Sequences with Machine Learning
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    Chapter 7 Targeting Differentially Co-regulated Genes by Multiobjective and Multimodal Optimization
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    Chapter 8 Modeling Genetic Networks: Comparison of Static and Dynamic Models
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    Chapter 9 A Genetic Embedded Approach for Gene Selection and Classification of Microarray Data
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    Chapter 10 Modeling the Shoot Apical Meristem in A. thaliana: Parameter Estimation for Spatial Pattern Formation
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    Chapter 11 Evolutionary Search for Improved Path Diagrams
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    Chapter 12 Simplifying Amino Acid Alphabets Using a Genetic Algorithm and Sequence Alignment
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    Chapter 13 Towards Evolutionary Network Reconstruction Tools for Systems Biology
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    Chapter 14 A Gaussian Evolutionary Method for Predicting Protein-Protein Interaction Sites
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    Chapter 15 Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks
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    Chapter 16 Tuning ReliefF for Genome-Wide Genetic Analysis
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    Chapter 17 Dinucleotide Step Parameterization of Pre-miRNAs Using Multi-objective Evolutionary Algorithms
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    Chapter 18 Amino Acid Features for Prediction of Protein-Protein Interface Residues with Support Vector Machines
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    Chapter 19 Predicting HIV Protease-Cleavable Peptides by Discrete Support Vector Machines
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    Chapter 20 Inverse Protein Folding on 2D Off-Lattice Model: Initial Results and Perspectives
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    Chapter 21 Virtual Error: A New Measure for Evolutionary Biclustering
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    Chapter 22 Characterising DNA/RNA Signals with Crisp Hypermotifs: A Case Study on Core Promoters
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    Chapter 23 Evaluating Evolutionary Algorithms and Differential Evolution for the Online Optimization of Fermentation Processes
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    Chapter 24 The Role of a Priori Information in the Minimization of Contact Potentials by Means of Estimation of Distribution Algorithms
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    Chapter 25 Classification of Cell Fates with Support Vector Machine Learning
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    Chapter 26 Reconstructing Linear Gene Regulatory Networks
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    Chapter 27 Individual-Based Modeling of Bacterial Foraging with Quorum Sensing in a Time-Varying Environment
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    Chapter 28 Substitution Matrix Optimisation for Peptide Classification
Attention for Chapter 8: Modeling Genetic Networks: Comparison of Static and Dynamic Models
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Chapter title
Modeling Genetic Networks: Comparison of Static and Dynamic Models
Chapter number 8
Book title
Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics
Published by
Springer Berlin Heidelberg, April 2007
DOI 10.1007/978-3-540-71783-6_8
Book ISBNs
978-3-54-071782-9, 978-3-54-071783-6
Authors

Cristina Rubio-Escudero, Oscar Harari, Oscar Cordón, Igor Zwir

Editors

Elena Marchiori, Jason H. Moore, Jagath C. Rajapakse

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 11%
Unknown 8 89%
Readers by discipline Count As %
Business, Management and Accounting 1 11%
Unknown 8 89%