Applications of Computational Intelligence in Biology

1. Front Matter

   Pages I-XXVI

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2. Techniques and Methodologies

   1. Statistically Based Pattern Discovery Techniques for Biological Data Analysis

      • Andrew Hamilton-Wright, Daniel W. Stashuk

      Pages 3-31

   2. Rough Sets In Data Analysis: Foundations and Applications

      • Lech Polkowski, Piotr Artiemjew

      Pages 33-54

   3. Evolving Solutions: The Genetic Algorithm and Evolution Strategies for Finding Optimal Parameters

      • Thomas McTavish, Diego Restrepo

      Pages 55-78

   4. An Introduction to Multi-Objective Evolutionary Algorithms and Some of Their Potential Uses in Biology

      • Antonio López Jaimes, Carlos A. Coello Coello

      Pages 79-102

3. Current Trends

   1. Local Classifiers as a Method of Analysing and Classifying Signals

      • Wit Jakuczun

      Pages 105-133

   2. Using Neural Models for Evaluation of Biological Activity of Selected Chemical Compounds

      • Ryszard Tadeusiewicz

      Pages 135-159

   3. Using Machine Vision to Detect Distinctive Behavioral Phenotypes of Thread-shape Microscopic Organism

      • Bai-Tao Zhou, Joong-Hwan Baek

      Pages 161-182

   4. Contour Matching for Fish Species Recognition and Migration Monitoring

      • Dah-Jye Lee, James K. Archibald, Robert B. Schoenberger, Aaron W. Dennis, Dennis K. Shiozawa

      Pages 183-207

   5. Using Random Forests to Provide Predicted Species Distribution Maps as a Metric for Ecological Inventory & Monitoring Programs

      • Dawn R. Magness, Falk Huettmann, John M. Morton

      Pages 209-229

   6. Visualization and Interactive Exploration of Large, Multidimensional Data Sets

      • John T. Langton, Elizabeth A. Gifford, Timothy J. Hickey

      Pages 231-255

4. Open Problems

   1. Phylogenomics, Protein Family Evolution, and the Tree of Life: An Integrated Approach between Molecular Evolution and Computational Intelligence

      • Laila A. Nahum, Sergio L. Pereira

      Pages 259-279

   2. Computational Aspects of Aggregation in Biological Systems

      • Vladik Kreinovich, Max Shpak

      Pages 281-305

   3. Conceptual Biology Research Supporting Platform: Current Design and Future Directions

      • Ying Xie, Jayasimha Katukuri, Vijay V. Raghavan, Tony Presti

      Pages 307-324

   4. Computational Intelligence in Electrophysiology: Trends and Open Problems

      • Cengiz Günay, Tomasz G. Smolinski, William W. Lytton, Thomas M. Morse, Padraig Gleeson, Sharon Crook et al.

      Pages 325-359

5. Cognitive Biology

   1. Using Broad Cognitive Models to Apply Computational Intelligence to Animal Cognition

      • Stan Franklin, Michael H. Ferkin

      Pages 363-394

   2. Epistemic Constraints on Autonomous Symbolic Representation in Natural and Artificial Agents

      • David Windridge, Josef Kittler

      Pages 395-422

6. Back Matter

   Pages 423-428

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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.

• algorithms
• behavior
• biology
• calculus
• cognition
• computational intelligence
• data analysis
• evolution
• evolutionary algorithm
• genetic algorithms
• genome
• intelligence
• protein
• protein family
• visualization

• Department of Biology, Emory University, Atlanta, USA

  Tomasz G. Smolinski

• Department of Computer Science, University of Arkansas at Little Rock, Little Rock, USA

  Mariofanna G. Milanova

• Department of Quantitative Methods and Information Systems College of Business and Administration, Kuwait University, Safat, Kuwait

  Aboul-Ella Hassanien

• Department of Information Technology Faculty of Computers and Information, Cairo University, Orman, Giza, Egypt

  Aboul-Ella Hassanien

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