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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics: 10th European Conference, EvoBIO 2012, Málaga, Spain, April 11-13, 2012, Proceedings: Lecture Notes in Computer Science, cartea 7246

Editat de Mario Giacobini, Leonardo Vanneschi, William S. Bush
en Limba Engleză Paperback – 28 mar 2012
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.
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Specificații

ISBN-13: 9783642290657
ISBN-10: 3642290655
Pagini: 272
Ilustrații: XIII, 255 p. 76 illus.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.41 kg
Ediția:2012
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Theoretical Computer Science and General Issues

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Textul de pe ultima copertă

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.

Caracteristici

Fast track conference proceedings Unique visibility State of the art research

Cuprins

Multiple Threshold Spatially Uniform ReliefF for the Genetic Analysis of Complex Human Diseases.- Time-Point Specific Weighting Improves Coexpression Networks from Time-Course Experiments.- Inferring Human Phenotype Networks from Genome-Wide Genetic.- Knowledge-Constrained K-Medoids Clustering of Regulatory Rare Alleles for Burden Tests.- Feature Selection and Classification of High Dimensional Mass Spectrometry Data: A Genetic Programming Approach.- Structured Populations and the Maintenance of Sex.- Hybrid Multiobjective Artificial Bee Colony with Differential Evolution Applied to Motif Finding.- ACO-Based Bayesian Network Ensembles for the Hierarchical Classification of Ageing-Related Proteins.- Dimensionality Reduction via Isomap with Lock-Step and Elastic Measures for Time Series Gene Expression Classification.- Supervising Random Forest Using Attribute Interaction Networks.- Hybrid Genetic Algorithms for Stress Recognition in Optimal Use of Biological Expert Knowledge from Literature.- Mining in Ant Colony Optimization for Analysis of Epistasis in Human Disease.- A Multiobjective Proposal Based on the Firefly Algorithm for Inferring Phylogenies.- Mining for Variability in the Coagulation Pathway: A Systems Biology Approach.- Improving the Performance of CGPANN for Breast Cancer Diagnosis Using Crossover and Radial Basis Functions.- An Evolutionary Approach to Wetlands Design.- Impact of Different Recombination Methods in a Mutation-SpecificMOEA for a Biochemical Application.- Cell–Based Metrics Improve the Detection of Gene-Gene Interactions Using Multifactor Dimensionality Reduction.- Emergence of Motifs in Model Gene Regulatory Networks.