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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
  2. Altmetric Badge
    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 16: Tuning ReliefF for Genome-Wide Genetic Analysis
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Chapter title
Tuning ReliefF for Genome-Wide Genetic Analysis
Chapter number 16
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_16
Book ISBNs
978-3-54-071782-9, 978-3-54-071783-6
Authors

Jason H. Moore, Bill C. White

Editors

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

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 9%
Netherlands 1 2%
Germany 1 2%
Unknown 41 87%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 26%
Student > Ph. D. Student 10 21%
Researcher 9 19%
Professor > Associate Professor 8 17%
Student > Doctoral Student 4 9%
Other 3 6%
Unknown 1 2%
Readers by discipline Count As %
Computer Science 19 40%
Agricultural and Biological Sciences 15 32%
Engineering 4 9%
Medicine and Dentistry 3 6%
Mathematics 1 2%
Other 3 6%
Unknown 2 4%