Applications of Computational Intelligence in Biology

Current Trends and Open Problems
1. Auflage. Softcover version of original hardcover Auflage 2008
ISBN: 978-3-642-09730-0
Verlag: Springer

Buch, Englisch, 428 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 694 g

Reihe: Studies in Computational Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Smolinski, Tomasz G.

Milanova, Mariofanna G.

Hassanien, Aboul-Ella

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie

Inhaltsverzeichnis

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.