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.
Author(s): Andrew Hamilton-Wright, Daniel W. Stashuk (auth.), Dr. Tomasz G. Smolinski, Professor Mariofanna G. Milanova, Professor Aboul-Ella Hassanien (eds.)
Series: Studies in Computational Intelligence 122
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2008
Language: English
Pages: 428
Tags: Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics); Bioinformatics; Computer Appl. in Life Sciences
Front Matter....Pages I-XXVI
Statistically Based Pattern Discovery Techniques for Biological Data Analysis....Pages 3-31
Rough Sets In Data Analysis: Foundations and Applications....Pages 33-54
Evolving Solutions: The Genetic Algorithm and Evolution Strategies for Finding Optimal Parameters....Pages 55-78
An Introduction to Multi-Objective Evolutionary Algorithms and Some of Their Potential Uses in Biology....Pages 79-102
Local Classifiers as a Method of Analysing and Classifying Signals....Pages 105-133
Using Neural Models for Evaluation of Biological Activity of Selected Chemical Compounds....Pages 135-159
Using Machine Vision to Detect Distinctive Behavioral Phenotypes of Thread-shape Microscopic Organism....Pages 161-182
Contour Matching for Fish Species Recognition and Migration Monitoring....Pages 183-207
Using Random Forests to Provide Predicted Species Distribution Maps as a Metric for Ecological Inventory & Monitoring Programs....Pages 209-229
Visualization and Interactive Exploration of Large, Multidimensional Data Sets....Pages 231-255
Phylogenomics, Protein Family Evolution, and the Tree of Life: An Integrated Approach between Molecular Evolution and Computational Intelligence....Pages 259-279
Computational Aspects of Aggregation in Biological Systems....Pages 281-305
Conceptual Biology Research Supporting Platform: Current Design and Future Directions....Pages 307-324
Computational Intelligence in Electrophysiology: Trends and Open Problems....Pages 325-359
Using Broad Cognitive Models to Apply Computational Intelligence to Animal Cognition....Pages 363-394
Epistemic Constraints on Autonomous Symbolic Representation in Natural and Artificial Agents....Pages 395-422
Back Matter....Pages 423-428
