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Applications of Computational Intelligence in Biology: Current Trends and Open Problems 2008 ed. [Hardback]

  • Formāts: Hardback, 428 pages, height x width: 235x155 mm, weight: 846 g, XXVI, 428 p., 1 Hardback
  • Sērija : Studies in Computational Intelligence 122
  • Izdošanas datums: 10-Jun-2008
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540785337
  • ISBN-13: 9783540785330
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  • Formāts: Hardback, 428 pages, height x width: 235x155 mm, weight: 846 g, XXVI, 428 p., 1 Hardback
  • Sērija : Studies in Computational Intelligence 122
  • Izdošanas datums: 10-Jun-2008
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540785337
  • ISBN-13: 9783540785330
Citas grāmatas par šo tēmu:

This book presents current applications of Computational Intelligence in Biology. It provides a medium for an exchange of expertise and concerns. The editors have solicited contributions from both computational intelligence as well as biology researchers.



Computational Intelligence (CI) has been a tremendously active area of - search for the past decade or so. There are many successful applications of CI in many sub elds of biology, including bioinformatics, computational - nomics, protein structure prediction, or neuronal systems modeling and an- ysis. However, there still are many open problems in biology that are in d- perate need of advanced and e cient computational methodologies to deal with tremendous amounts of data that those problems are plagued by. - fortunately, biology researchers are very often unaware of the abundance of computational techniques that they could put to use to help them analyze and understand the data underlying their research inquiries. On the other hand, computational intelligence practitioners are often unfamiliar with the part- ular problems that their new, state-of-the-art algorithms could be successfully applied for. The separation between the two worlds is partially caused by the use of di erent languages in these two spheres of science, but also by the relatively small number of publications devoted solely to the purpose of fac- itating the exchange of new computational algorithms and methodologies on one hand, and the needs of the biology realm on the other. The purpose of this book is to provide a medium for such an exchange of expertise and concerns. In order to achieve the goal, we have solicited cont- butions from both computational intelligence as well as biology researchers.
Techniques and Methodologies.- Statistically Based Pattern Discovery
Techniques for Biological Data Analysis.- Rough Sets In Data Analysis:
Foundations and Applications.- Evolving Solutions: The Genetic Algorithm and
Evolution Strategies for Finding Optimal Parameters.- An Introduction to
Multi-Objective Evolutionary Algorithms and Some of Their Potential Uses in
Biology.- Current Trends.- Local Classifiers as a Method of Analysing and
Classifying Signals.- Using Neural Models for Evaluation of Biological
Activity of Selected Chemical Compounds.- Using Machine Vision to Detect
Distinctive Behavioral Phenotypes of Thread-shape Microscopic Organism.-
Contour Matching for Fish Species Recognition and Migration Monitoring.-
Using Random Forests to Provide Predicted Species Distribution Maps as a
Metric for Ecological Inventory & Monitoring Programs.- Visualization and
Interactive Exploration of Large, Multidimensional Data Sets.- Open
Problems.- Phylogenomics, Protein Family Evolution, and the Tree of Life: An
Integrated Approach between Molecular Evolution and Computational
Intelligence.- Computational Aspects of Aggregation in Biological Systems.-
Conceptual Biology Research Supporting Platform: Current Design and Future
Directions.- Computational Intelligence in Electrophysiology: Trends and Open
Problems.- Cognitive Biology.- Using Broad Cognitive Models to Apply
Computational Intelligence to Animal Cognition.- Epistemic Constraints on
Autonomous Symbolic Representation in Natural and Artificial Agents.