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• PART I - Introduction to the Concepts of Bioinformatics and Evolutionary Computation
• Chapter 1. An Introduction to Bioinformatics for Computer Scientists By David W. Corne and Gary B. Fogel
• Chapter 2. An Introduction to Evolutionary Computation for Biologists By Gary B. Fogel and David W. Corne PART II - Sequence and Structure Alignment
• Chapter 3. Determining Genome Sequences from Experimental Data Using Evolutionary Computation By Jacek Blazewic and Marta Kasprzak
• Chapter 4. Protein Structure Alignment Using Evolutionary Computation By Joseph D. Szustakowski and Zhipeng Weng
• Chapter 5. Using Genetic Algorithms for Pairwise and Multiple Sequence Alignments By Cedric Notredame PART III - Protein Folding
• Chapter 6. On the Evolutionary Search for Solutions to the Protein Folding Problem By Garrison W. Greenwood and Jae-Min Shin
• Chapter 7. Toward Effective Polypeptide Structure Prediction with Parallel Fast Messy Genetic Algorithms By Gary B. Lamont and Laurence D. Merkle
• Chapter 8. Application of Evolutionary Computation to Protein Folding with Specialized Operators By Steffen Schulze-Kremer PART IV - Machine Learning and Inference
• Chapter 9. Identification of Coding Regions in DNA Sequences Using Evolved Neural Networks By Gary B. Fogel, Kumar Chellapilla, and David B. Fogel
• Chapter 10. Clustering Microarray Data with Evolutionary Algorithms By Emanuel Falkenauer and Arnaud Marchand
• Chapter 11. Evolutionary Computation and Fractal Visualization of Sequence Data By Dan Ashlock and Jim Golden
• Chapter 12. Identifying Metabolic Pathways and Gene Regulation Networks with Evolutionary Algorithms By Junji Kitagawa and Hitoshi Iba
• Chapter 13. Evolutionary Computational Support for the Characterization of Biological Systems By Bogdan Filipic and Janez Strancar PART V - Feature Selection
• Chapter 14. Discovery of Genetic and Environmental Interactions in Disease Data Using Evolutionary Computation By Laetitia Jourdan, Clarisse Dhaenens[AQ2], and El-Ghazali Talbi
• Chapter 15. Feature Selection Methods Based on Genetic Algorithms for in Silico Drug Design By Mark J. Embrechts, Muhsin Ozdemir, Larry Lockwood, Curt Breneman, Kristin Bennet, Dirk Devogelaere, and Marcel Rijkaert
• Chapter 16. Interpreting Analytical Spectra with Evolutionary Computation By Jem J. Rowland Appendix: Internet Resources for Bioinformatics Data and Tools.
• (source: Nielsen Book Data)

Bioinformatics has never been as popular as it is today. The genomics revolution is generating so much data in such rapid succession that it has become difficult for biologists to decipher. In particular, there are many problems in biology that are too large to solve with standard methods. Researchers in evolutionary computation (EC) have turned their attention to these problems. They understand the power of EC to rapidly search very large and complex spaces and return reasonable solutions. While these researchers are increasingly interested in problems from the biological sciences, EC and its problem-solving capabilities are generally not yet understood or applied in the biology community. This book offers a definitive resource to bridge the computer science and biology communities. Gary Fogel and David Corne, well-known representatives of these fields, introduce biology and bioinformatics to computer scientists, and evolutionary computation to biologists and computer scientists unfamiliar with these techniques. The fourteen chapters that follow are written by leading computer scientists and biologists who examine successful applications of evolutionary computation to various problems in the biological sciences. It describes applications of EC to bioinformatics in a wide variety of areas including DNA sequencing, protein folding, gene and protein classification, drug targeting, drug design, data mining of biological databases, and biodata visualization. It offers industrial and academic researchers in computer science, biology, and bioinformatics an important resource for applying evolutionary computation. It includes a detailed appendix of biological data resources.
(source: Nielsen Book Data)

A study of evolutionary processes that involve the combination of systems of semi-independently preadapted genetic material rather than the linear or sequential accumulation of slight modifications; with computational models that illustrate these mechanisms. No biological concept has had greater impact on the way we view ourselves and the world around us than the theory of evolution by natural selection. Darwin's masterful contribution was to provide an algorithmic model (a formal step-by-step procedure) of how adaptation may take place in biological systems. However, the simple process of linear incremental improvement that he described is only one algorithmic possibility, and certain biological phenomena provide the possibility of implementing alternative processes. In Compositional Evolution. Richard Watson uses the tools of computer science and computational biology to show that certain mechanisms of genetic variation (such as sex, gene transfer, and symbiosis) allowing the combination of preadapted genetic material enable an evolutionary process, compositional evolution, that is algorithmically distinct from the Darwinian gradualist framework. After reviewing the gradualist framework of evolution and outlining the analogous principles at work in evolutionary computation, Watson describes the compositional mechanisms of evolutionary biology and provides computational models that illustrate his argument. He uses models such as the genetic algorithm as well as novel models to explore different evolutionary scenarios, comparing evolution based on spontaneous point mutation, sexual recombination, and symbiotic encapsulation. He shows that the models of sex and symbiosis are algorithmically distinct from simpler stochastic optimisation methods based on gradual processes. Finally, Watson discusses the impact of compositional evolution on our understanding of natural evolution and, similarly, the utility of evolutionary computation methods for problem solving and design.
(source: Nielsen Book Data)

• An introduction to bioinformatics for computer scientists / David W. Corne and Gary B. Fogel
• An introduction to evolutionary computation for biologists / Gary B. Fogel and David W. Corne
• Determining genome sequences from experimental data using evolutionary computation / Jacek Blazewicz and Marta Kasprzak
• Protein structure alignment using evolutionary computation / Joseph D. Szustakowski and Zhiping Weng
• Using genetic algorithms for pairwise and multiple sequence alignments / Cédric Notredame
• On the evolutionary search for solutions to the protein folding problem / Garrison W. Greenwood and Jae-Min Shin
• Toward effective polypeptide structure prediction with parallel fast messy genetic algorithms / Gary B. Lamont and Laurence D. Merkle
• Application of evolutionary computation to protein folding with specialized operators / Steffen Schulze-Kremer
• Identification of coding regions in DNA sequences using evolved neural networks / Gary B. Fogel, Kumar Chellapilla and David B. Fogel
• Clustering microarray data with evolutionary algorithms / Emanuel Falkenauer and Arnaud Marchand
• Evolutionary computation and fractal visualization of sequence data / Dan Ashlock and Jim Golden
• Identifying metabolic pathways and gene regulation networks with evolutionary algorithms / Junji Kitagawa and Hitoshi Iba --Evolutionary computational support for the characterization of biological systems / Bogdan Filipič and Janez Štrancar
• Discovery of genetic and environmental interactions in disease data using evolutionary computation / Laetitia Jourdan, Clarisse Dhaenens-Flipo and El-Ghazali Talbi
• Feature selection methods based on genetic algorithms for in silico drug design / Mark J. Embrechts, Muhsin Ozdemir, Larry Lockwood, Curt Breneman, Kristin Bennett, Dirk Devogelaere and Marcel Rijckaert --Interpreting analytical spectra with evolutionary computation / Jem J. Rowland.

Bioinformatics has never been as popular as it is today. The genomics revolution is generating so much data in such rapid succession that it has become difficult for biologists to decipher. In particular, there are many problems in biology that are too large to solve with standard methods. Researchers in evolutionary computation (EC) have turned their attention to these problems. They understand the power of EC to rapidly search very large and complex spaces and return reasonable solutions. While these researchers are increasingly interested in problems from the biological sciences, EC and its problem-solving capabilities are generally not yet understood or applied in the biology community. This book offers a definitive resource to bridge the computer science and biology communities. Gary Fogel and David Corne, well-known representatives of these fields, introduce biology and bioinformatics to computer scientists, and evolutionary computation to biologists and computer scientists unfamiliar with these techniques. The fourteen chapters that follow are written by leading computer scientists and biologists who examine successful applications of evolutionary computation to various problems in the biological sciences. * Describes applications of EC to bioinformatics in a wide variety of areas including DNA sequencing, protein folding, gene and protein classification, drug targeting, drug design, data mining of biological databases, and biodata visualization. * Offers industrial and academic researchers in computer science, biology, and bioinformatics an important resource for applying evolutionary computation. * Includes a detailed appendix of biological data resources.

This book constitutes the refereed proceedings of the 9th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2011, held in Torino, Italy, in April 2011 co-located with the Evo 2011 events. The 12 revised full papers presented together with 7 poster papers were carefully reviewed and selected from numerous submissions. All papers included topics of interest such as biomarker discovery, cell simulation and modeling, ecological modeling, fluxomics, gene networks, biotechnology, metabolomics, microarray analysis, phylogenetics, protein interactions, proteomics, sequence analysis and alignment, and systems biology.
(source: Nielsen Book Data)

• Identifying Regulatory Sites Using Neighborhood Species.- Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs.- Hypothesis Testing with Classifier Systems for Rule-Based Risk Prediction.- Robust Peak Detection and Alignment of nanoLC-FT Mass Spectrometry Data.- One-Versus-One and One-Versus-All Multiclass SVM-RFE for Gene Selection in Cancer Classification.- Understanding Signal Sequences with Machine Learning.- Targeting Differentially Co-regulated Genes by Multiobjective and Multimodal Optimization.- Modeling Genetic Networks: Comparison of Static and Dynamic Models.- A Genetic Embedded Approach for Gene Selection and Classification of Microarray Data.- Modeling the Shoot Apical Meristem in A. thaliana: Parameter Estimation for Spatial Pattern Formation.- Evolutionary Search for Improved Path Diagrams.- Simplifying Amino Acid Alphabets Using a Genetic Algorithm and Sequence Alignment.- Towards Evolutionary Network Reconstruction Tools for Systems Biology.- A Gaussian Evolutionary Method for Predicting Protein-Protein Interaction Sites.- Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks.- Tuning ReliefF for Genome-Wide Genetic Analysis.- Dinucleotide Step Parameterization of Pre-miRNAs Using Multi-objective Evolutionary Algorithms.- Amino Acid Features for Prediction of Protein-Protein Interface Residues with Support Vector Machines.- Predicting HIV Protease-Cleavable Peptides by Discrete Support Vector Machines.- Inverse Protein Folding on 2D Off-Lattice Model: Initial Results and Perspectives.- Virtual Error: A New Measure for Evolutionary Biclustering.- Characterising DNA/RNA Signals with Crisp Hypermotifs: A Case Study on Core Promoters.- Evaluating Evolutionary Algorithms and Differential Evolution for the Online Optimization of Fermentation Processes.- The Role of a Priori Information in the Minimization of Contact Potentials by Means of Estimation of Distribution Algorithms.- Classification of Cell Fates with Support Vector Machine Learning.- Reconstructing Linear Gene Regulatory Networks.- Individual-Based Modeling of Bacterial Foraging with Quorum Sensing in a Time-Varying Environment.- Substitution Matrix Optimisation for Peptide Classification.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain, April 2007. Coverage brings together experts in computer science with experts in bioinformatics and the biological sciences. It presents contributions on fundamental and theoretical issues along with papers dealing with different applications areas.
(source: Nielsen Book Data)

• Variable Genetic Operator Search for the Molecular Docking Problem.- Variable Genetic Operator Search for the Molecular Docking Problem.- Role of Centrality in Network-Based Prioritization of Disease Genes.- Parallel Multi-Objective Approaches for Inferring Phylogenies.- An Evolutionary Model Based on Hill-Climbing Search Operators for Protein Structure Prediction.- Finding Gapped Motifs by a Novel Evolutionary Algorithm.- Top-Down Induction of Phylogenetic Trees.- A Model Free Method to Generate Human Genetics Datasets with Complex Gene-Disease Relationships.- Grammatical Evolution of Neural Networks for Discovering Epistasis among Quantitative Trait Loci.- Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions.- Identification of Individualized Feature Combinations for Survival Prediction in Breast Cancer: A Comparison of Machine Learning Techniques.- Correlation-Based Scatter Search for Discovering Biclusters from Gene Expression Data.- A Local Search Appproach for Transmembrane Segment and Signal Peptide Discrimination.- A Replica Exchange Monte Carlo Algorithm for the Optimization of Secondary Structure Packing in Proteins.- Improving Multi-Relief for Detecting Specificity Residues from Multiple Sequence Alignments.- Using Probabilistic Dependencies Improves the Search of Conductance-Based Compartmental Neuron Models.- Posters.- The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics.- Artificial Immune Systems for Epistasis Analysis in Human Genetics.- Metaheuristics for Strain Optimization Using Transcriptional Information Enriched Metabolic Models.- Using Rotation Forest for Protein Fold Prediction Problem: An Empirical Study.- Towards Automatic Detecting of Overlapping Genes - Clustered BLAST Analysis of Viral Genomes.- Investigating Populational Evolutionary Algorithms to Add Vertical Meaning in Phylogenetic Trees.
• (source: Nielsen Book Data)

The ?eld of bioinformatics has two main objectives: the creation and main- nance of biological databases, and the discovery of knowledge from life sciences datainordertounravelthemysteriesofbiologicalfunction, leadingtonewdrugs andtherapiesforhumandisease. Life sciencesdatacomeinthe formofbiological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model speci?c information in a given dataset in order to generate new interesting knowledge. Computer science methods such as evolutionary computation, machine learning, and data mining all have a great deal to o?er the ?eld of bioinformatics. The goal of the 8th - ropean Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics (EvoBIO 2010) was to bring together experts in these ?elds in order to discuss new and novel methods for tackling complex biological problems. The 8th EvoBIO conference was held in Istanbul, Turkey during April 7-9, 2010attheIstanbulTechnicalUniversity. EvoBIO2010washeldjointlywiththe 13th European Conference on Genetic Programming (EuroGP 2010), the 10th European Conference on Evolutionary Computation in Combinatorial Opti- sation (EvoCOP 2010), and the conference on the applications of evolutionary computation, EvoApplications. Collectively, the conferences areorganizedunder the name Evo* (www. evostar. org). EvoBIO, held annually as a workshop since 2003, became a conference in 2007 and it is now the premiere European event for those interested in the interface between evolutionary computation, machine learning, data mining, bioinformatics, and computational biology.
(source: Nielsen Book Data)

• Automatic Task Decomposition for the NeuroEvolution of Augmenting Topologies (NEAT) Algorithm / Timmy Manning and Paul Walsh
• Evolutionary Reaction Systems / Luca Manzoni, Mauro Castelli and Leonardo Vanneschi
• Optimizing the Edge Weights in Optimal Assignment Methods for Virtual Screening with Particle Swarm Optimization / Lars Rosenbaum, Andreas Jahn and Andreas Zell
• Lévy-Flight Genetic Programming: Towards a New Mutation Paradigm / Christian Darabos, Mario Giacobini, Ting Hu and Jason H. Moore
• Understanding Zooplankton Long Term Variability through Genetic Programming / Simone Marini and Alessandra Conversi
• Inferring Disease-Related Metabolite Dependencies with a Bayesian Optimization Algorithm / Holger Franken, Alexander Seitz, Rainer Lehmann, Hans-Ulrich Häring and Norbert Stefan, et al.
• A GPU-Based Multi-swarm PSO Method for Parameter Estimation in Stochastic Biological Systems Exploiting Discrete-Time Target Series / Marco S. Nobile, Daniela Besozzi, Paolo Cazzaniga, Giancarlo Mauri and Dario Pescini
• Tracking the Evolution of Cooperation in Complex Networked Populations / Flávio L. Pinheiro, Francisco C. Santos and Jorge M. Pacheco
• GeNet: A Graph-Based Genetic Programming Framework for the Reverse Engineering of Gene Regulatory Networks / Leonardo Vanneschi, Matteo Mondini, Martino Bertoni, Alberto Ronchi and Mattia Stefano
• Comparing Multiobjective Artificial Bee Colony Adaptations for Discovering DNA Motifs / David L. González-Álvarez, Miguel A. Vega-Rodríguez, Juan A. Gómez-Pulido and Juan M. Sánchez-Pérez
• The Role of Mutations in Whole Genome Duplication / Qinxin Pan, Christian Darabos and Jason H. Moore
• Comparison of Methods for Meta-dimensional Data Analysis Using in Silico and Biological Data Sets / Emily R. Holzinger, Scott M. Dudek, Alex T. Frase, Brooke Fridley and Prabhakar Chalise, et al.

• Inferring Phylogenetic Trees Using a Multiobjective Artificial Bee Colony Algorithm / Sergio Santander-Jiménez, Miguel A. Vega-Rodríguez, Juan A. Gómez-Pulido and Juan M. Sánchez-Pérez
• Prediction of Mitochondrial Matrix Protein Structures Based on Feature Selection and Fragment Assembly / Gualberto Asencio-Cortés, Jesús S. Aguilar-Ruiz, Alfonso E. Márquez-Chamorro, Roberto Ruiz and Cosme E. Santiesteban-Toca
• Feature Selection for Lung Cancer Detection Using SVM Based Recursive Feature Elimination Method / Kesav Kancherla and Srinivas Mukkamala
• Measuring Gene Expression Noise in Early Drosophila Embryos: The Highly Dynamic Compartmentalized Micro-environment of the Blastoderm Is One of the Main Sources of Noise / Alexander V. Spirov, Nina E. Golyandina, David M. Holloway, Theodore Alexandrov and Ekaterina N. Spirova, et al.
• Artificial Immune Systems Perform Valuable Work When Detecting Epistasis in Human Genetic Datasets / Delaney Granizo-Mackenzie and Jason H. Moore
• A Biologically Informed Method for Detecting Associations with Rare Variants / Carrie C. Buchanan, John R. Wallace, Alex T. Frase, Eric S. Torstenson and Sarah A. Pendergrass, et al.
• Complex Detection in Protein-Protein Interaction Networks: A Compact Overview for Researchers and Practitioners / Clara Pizzuti, Simona E. Rombo and Elena Marchiori
• Short-Range Interactions and Decision Tree-Based Protein Contact Map Predictor / Cosme E. Santiesteban-Toca, Gualberto Asencio-Cortés, Alfonso E. Márquez-Chamorro and Jesús S. Aguilar-Ruiz
• A NSGA-II Algorithm for the Residue-Residue Contact Prediction / Alfonso E. Márquez-Chamorro, Federico Divina, Jesús S. Aguilar-Ruiz, Jaume Bacardit and Gualberto Asencio-Cortés, et al.
• In Silico Infection of the Human Genome / W.B. Langdon and M.J. Arno
• Improving Phylogenetic Tree Interpretability by Means of Evolutionary Algorithms / Francesco Cerutti, Luigi Bertolotti, Tony L. Goldberg and Mario Giacobini.

This book constitutes the refereed proceedings of the 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012, held in Málaga, Spain, in April 2012 co-located with the Evo* 2012 events. The 15 revised full papers presented together with 8 poster papers were carefully reviewed and selected from numerous submissions. Computational Biology is a wide and varied discipline, incorporating aspects of statistical analysis, data structure and algorithm design, machine learning, and mathematical modeling toward the processing and improved understanding of biological data. Experimentalists now routinely generate new information on such a massive scale that the techniques of computer science are needed to establish any meaningful result. As a consequence, biologists now face the challenges of algorithmic complexity and tractability, and combinatorial explosion when conducting even basic analyses.

• Identifying Regulatory Sites Using Neighborhood Species.- Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs.- Hypothesis Testing with Classifier Systems for Rule-Based Risk Prediction.- Robust Peak Detection and Alignment of nanoLC-FT Mass Spectrometry Data.- One-Versus-One and One-Versus-All Multiclass SVM-RFE for Gene Selection in Cancer Classification.- Understanding Signal Sequences with Machine Learning.- Targeting Differentially Co-regulated Genes by Multiobjective and Multimodal Optimization.- Modeling Genetic Networks: Comparison of Static and Dynamic Models.- A Genetic Embedded Approach for Gene Selection and Classification of Microarray Data.- Modeling the Shoot Apical Meristem in A. thaliana: Parameter Estimation for Spatial Pattern Formation.- Evolutionary Search for Improved Path Diagrams.- Simplifying Amino Acid Alphabets Using a Genetic Algorithm and Sequence Alignment.- Towards Evolutionary Network Reconstruction Tools for Systems Biology.- A Gaussian Evolutionary Method for Predicting Protein-Protein Interaction Sites.- Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks.- Tuning ReliefF for Genome-Wide Genetic Analysis.- Dinucleotide Step Parameterization of Pre-miRNAs Using Multi-objective Evolutionary Algorithms.- Amino Acid Features for Prediction of Protein-Protein Interface Residues with Support Vector Machines.- Predicting HIV Protease-Cleavable Peptides by Discrete Support Vector Machines.- Inverse Protein Folding on 2D Off-Lattice Model: Initial Results and Perspectives.- Virtual Error: A New Measure for Evolutionary Biclustering.- Characterising DNA/RNA Signals with Crisp Hypermotifs: A Case Study on Core Promoters.- Evaluating Evolutionary Algorithms and Differential Evolution for the Online Optimization of Fermentation Processes.- The Role of a Priori Information in the Minimization of Contact Potentials by Means of Estimation of Distribution Algorithms.- Classification of Cell Fates with Support Vector Machine Learning.- Reconstructing Linear Gene Regulatory Networks.- Individual-Based Modeling of Bacterial Foraging with Quorum Sensing in a Time-Varying Environment.- Substitution Matrix Optimisation for Peptide Classification.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain, April 2007. Coverage brings together experts in computer science with experts in bioinformatics and the biological sciences. It presents contributions on fundamental and theoretical issues along with papers dealing with different applications areas.
(source: Nielsen Book Data)

• Oral Contributions. Multiple Threshold Spatially Uniform ReliefF for the Genetic Analysis of Complex Human Diseases / Delaney Granizo-Mackenzie, Jason H. Moore
• Time-Point Specific Weighting Improves Coexpression Networks from Time-Course Experiments / Jie Tan, Gavin D. Grant, Michael L. Whitfield, Casey S. Greene
• Inferring Human Phenotype Networks from Genome-Wide Genetic Associations / Christian Darabos, Kinjal Desai, Richard Cowper-Sal·lari, Mario Giacobini
• Knowledge-Constrained K-Medoids Clustering of Regulatory Rare Alleles for Burden Tests / R. Michael Sivley, Alexandra E. Fish, William S. Bush
• Feature Selection and Classification of High Dimensional Mass Spectrometry Data: A Genetic Programming Approach / Soha Ahmed, Mengjie Zhang, Lifeng Peng
• Structured Populations and the Maintenance of Sex / Peter A. Whigham, Grant Dick, Alden Wright, Hamish G. Spencer
• Hybrid Multiobjective Artificial Bee Colony with Differential Evolution Applied to Motif Finding / David L. González-Álvarez, Miguel A. Vega-Rodríguez
• ACO-Based Bayesian Network Ensembles for the Hierarchical Classification of Ageing-Related Proteins / Khalid M. Salama, Alex A. Freitas
• Dimensionality Reduction via Isomap with Lock-Step and Elastic Measures for Time Series Gene Expression Classification / Carlotta Orsenigo, Carlo Vercellis
• Supervising Random Forest Using Attribute Interaction Networks / Qinxin Pan, Ting Hu, James D. Malley, Angeline S. Andrew, Margaret R. Karagas.

• Poster Contributions. Hybrid Genetic Algorithms for Stress Recognition in Reading / Nandita Sharma, Tom Gedeon
• Optimal Use of Biological Expert Knowledge from Literature Mining in Ant Colony Optimization for Analysis of Epistasis in Human Disease / Arvis Sulovari, Jeff Kiralis, Jason H. Moore
• A Multiobjective Proposal Based on the Firefly Algorithm for Inferring Phylogenies / Sergio Santander-Jiménez, Miguel A. Vega-Rodríguez
• Mining for Variability in the Coagulation Pathway: A Systems Biology Approach / Davide Castaldi, Daniele Maccagnola, Daniela Mari, Francesco Archetti
• Improving the Performance of CGPANN for Breast Cancer Diagnosis Using Crossover and Radial Basis Functions / Timmy Manning, Paul Walsh
• An Evolutionary Approach to Wetlands Design / Marco Gaudesi, Andrea Marion, Tommaso Musner, Giovanni Squillero, Alberto Tonda
• Impact of Different Recombination Methods in a Mutation-Specific MOEA for a Biochemical Application / Susanne Rosenthal, Nail El-Sourani, Markus Borschbach
• Cell-Based Metrics Improve the Detection of Gene-Gene Interactions Using Multifactor Dimensionality Reduction / Jonathan M. Fisher, Peter Andrews, Jeff Kiralis, Nicholas A. Sinnott-Armstrong
• Emergence of Motifs in Model Gene Regulatory Networks / Marcin Zagórski.

This book constitutes the refereed proceedings of the 11th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2013, held in Vienna, Austria, in April 2013, colocated with the Evo* 2013 events EuroGP, EvoCOP, EvoMUSART and EvoApplications. The 10 revised full papers presented together with 9 poster papers were carefully reviewed and selected from numerous submissions. The papers cover a wide range of topics in the field of biological data analysis and computational biology. They address important problems in biology, from the molecular and genomic dimension to the individual and population level, often drawing inspiration from biological systems in oder to produce solutions to biological problems.
(source: Nielsen Book Data)

• Association Study between Gene Expression and Multiple Relevant Phenotypes with Cluster Analysis.- Gaussian Graphical Models to Infer Putative Genes Involved in Nitrogen Catabolite Repression in S. cerevisiae.- Chronic Rat Toxicity Prediction of Chemical Compounds Using Kernel Machines.- Simulating Evolution of Drosophila Melanogaster Ebony Mutants Using a Genetic Algorithm.- Microarray Biclustering: A Novel Memetic Approach Based on the PISA Platform.- F-score with Pareto Front Analysis for Multiclass Gene Selection.- A Hierarchical Classification Ant Colony Algorithm for Predicting Gene Ontology Terms.- Conquering the Needle-in-a-Haystack: How Correlated Input Variables Beneficially Alter the Fitness Landscape for Neural Networks.- Optimal Use of Expert Knowledge in Ant Colony Optimization for the Analysis of Epistasis in Human Disease.- On the Efficiency of Local Search Methods for the Molecular Docking Problem.- A Comparison of Genetic Algorithms and Particle Swarm Optimization for Parameter Estimation in Stochastic Biochemical Systems.- Guidelines to Select Machine Learning Scheme for Classification of Biomedical Datasets.- Evolutionary Approaches for Strain Optimization Using Dynamic Models under a Metabolic Engineering Perspective.- Clustering Metagenome Short Reads Using Weighted Proteins.- A Memetic Algorithm for Phylogenetic Reconstruction with Maximum Parsimony.- Validation of a Morphogenesis Model of Drosophila Early Development by a Multi-objective Evolutionary Optimization Algorithm.- Refining Genetic Algorithm Based Fuzzy Clustering through Supervised Learning for Unsupervised Cancer Classification.
• (source: Nielsen Book Data)

The ?eld of bioinformatics has two main objectives: the creation and main- nance of biological databases, and the discovery of knowledge from life sciences datainordertounravelthemysteriesofbiologicalfunction, leadingtonewdrugs andtherapiesforhumandisease.Life sciencesdatacomeinthe formofbiological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model speci?c information in a given dataset in order to generate new interesting knowledge. Computer science methods such as evolutionary computation, machine learning, and data mining all have a great deal to o?er the ?eld of bioinformatics. The goal of the 7th - ropean Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics (EvoBIO 2009) was to bring together experts in these ?elds in order to discuss new and novel methods for tackling complex biological problems. The 7th EvoBIO conference was held in Tubing .. en, Germany during April 15-17, 2009 at the Eberhard-Karls-Universitat .. Tu ..bingen. EvoBIO 2009 was held jointly with the 12th European Conference on Genetic Programming (- roGP 2009), the 9th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2009), and the Evo Workshops. C- lectively, the conferences and workshops are organized under the name Evo* (www.evostar.org).
(source: Nielsen Book Data)

• PART I: MODELING CODON EVOLUTION
• PART II: CODON USAGE BIAS.
• (source: Nielsen Book Data)

Codon-based models of evolution are a relatively new addition to the toolkit of computational biologists, and in recent years remarkable progress has been made in this area. The study of evolution at the codon level captures information contained in both amino acid and synonymous DNA substitutions. By combining these two types of information, codon analyses are more powerful than those of either amino acid or DNA evolution alone. This is a clear benefit for most evolutionary analyses, including phylogenetic reconstruction, detection of selection, ancestral sequence reconstruction, and alignment of coding DNA. Despite the theoretical advantages of codon based models, their relative complexity delayed their widespread use. Only in recent years, when large-scale sequencing projects produced sufficient genomic data and computational power increased, did their usage become more common. In Codon Evolution, leading researchers in the field of molecular evolution provide the latest insights from codon-based analyses of genetic sequences. The first part of the book provides comprehensive coverage of the developments of various types of codon substitution models such as parametric and empirical models used in maximum likelihood as well as Bayesian frameworks. Subsequent chapters examine the use of codon models to infer selection and other applications of codon models to biological systems. The second part of the book focuses on codon usage bias. Both the underlying mechanisms as well as current methods to analyse codon usage bias are presented.
(source: Nielsen Book Data)

• PART I: MODELING CODON EVOLUTION
• PART II: CODON USAGE BIAS.
• (source: Nielsen Book Data)

Codon-based models of evolution are a relatively new addition to the toolkit of computational biologists, and in recent years remarkable progress has been made in this area. The study of evolution at the codon level captures information contained in both amino acid and synonymous DNA substitutions. By combining these two types of information, codon analyses are more powerful than those of either amino acid or DNA evolution alone. This is a clear benefit for most evolutionary analyses, including phylogenetic reconstruction, detection of selection, ancestral sequence reconstruction, and alignment of coding DNA. Despite the theoretical advantages of codon based models, their relative complexity delayed their widespread use. Only in recent years, when large-scale sequencing projects produced sufficient genomic data and computational power increased, did their usage become more common. In Codon Evolution, leading researchers in the field of molecular evolution provide the latest insights from codon-based analyses of genetic sequences. The first part of the book provides comprehensive coverage of the developments of various types of codon substitution models such as parametric and empirical models used in maximum likelihood as well as Bayesian frameworks. Subsequent chapters examine the use of codon models to infer selection and other applications of codon models to biological systems. The second part of the book focuses on codon usage bias. Both the underlying mechanisms as well as current methods to analyse codon usage bias are presented.
(source: Nielsen Book Data)