Applications of Computational Intelligence in Biology: Current Trends and Open Problems / Edition 1by Tomasz G. Smolinski
Pub. Date: 12/08/2010
Publisher: Springer Berlin Heidelberg
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… See more details below
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.
Table of Contents
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.
and post it to your social network
Most Helpful Customer Reviews
See all customer reviews >