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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 Association Study between Gene Expression and Multiple Relevant Phenotypes with Cluster Analysis.
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    Chapter 2 Gaussian Graphical Models to Infer Putative Genes Involved in Nitrogen Catabolite Repression in S. cerevisiae
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    Chapter 3 Chronic Rat Toxicity Prediction of Chemical Compounds Using Kernel Machines
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    Chapter 4 Simulating Evolution of Drosophila Melanogaster Ebony Mutants Using a Genetic Algorithm
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    Chapter 5 Microarray Biclustering: A Novel Memetic Approach Based on the PISA Platform
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    Chapter 6 F-score with Pareto Front Analysis for Multiclass Gene Selection
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    Chapter 7 A Hierarchical Classification Ant Colony Algorithm for Predicting Gene Ontology Terms
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    Chapter 8 Conquering the Needle-in-a-Haystack: How Correlated Input Variables Beneficially Alter the Fitness Landscape for Neural Networks
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    Chapter 9 Optimal Use of Expert Knowledge in Ant Colony Optimization for the Analysis of Epistasis in Human Disease
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    Chapter 10 On the Efficiency of Local Search Methods for the Molecular Docking Problem
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    Chapter 11 A Comparison of Genetic Algorithms and Particle Swarm Optimization for Parameter Estimation in Stochastic Biochemical Systems
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    Chapter 12 Guidelines to Select Machine Learning Scheme for Classification of Biomedical Datasets
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    Chapter 13 Evolutionary Approaches for Strain Optimization Using Dynamic Models under a Metabolic Engineering Perspective
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    Chapter 14 Clustering Metagenome Short Reads Using Weighted Proteins
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    Chapter 15 A Memetic Algorithm for Phylogenetic Reconstruction with Maximum Parsimony
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    Chapter 16 Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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    Chapter 17 Refining Genetic Algorithm Based Fuzzy Clustering through Supervised Learning for Unsupervised Cancer Classification
Attention for Chapter 13: Evolutionary Approaches for Strain Optimization Using Dynamic Models under a Metabolic Engineering Perspective
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