Knowledge Discovery and Emergent Complexity in Bioinformatics

1. Front Matter

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2. Knowledge Discovery and Emergent Complexity in Bioinformatics

   • Ronald Westra, Karl Tuyls, Yvan Saeys, Ann Nowé

   Pages 1-9

3. Boolean Algebraic Structures of the Genetic Code: Possibilities of Applications

   • Ricardo Grau, Maria del C. Chavez, Robersy Sanchez, Eberto Morgado, Gladys Casas, Isis Bonet

   Pages 10-21

4. Discovery of Gene Regulatory Networks in Aspergillus fumigatus

   • Reinhard Guthke, Olaf Kniemeyer, Daniela Albrecht, Axel A. Brakhage, Ulrich Möller

   Pages 22-41

5. Complexity Measures for Gene Assembly

   • Tero Harju, Chang Li, Ion Petre, Grzegorz Rozenberg

   Pages 42-60

6. Learning Relations from Biomedical Corpora Using Dependency Trees

   • Sophia Katrenko, Pieter Adriaans

   Pages 61-80

7. Advancing the State of the Art in Computational Gene Prediction

   • William H. Majoros, Uwe Ohler

   Pages 81-106

8. Enhancing Coding Potential Prediction for Short Sequences Using Complementary Sequence Features and Feature Selection

   • Yvan Saeys, Yves Van de Peer

   Pages 107-118

9. The NetGenerator Algorithm: Reconstruction of Gene Regulatory Networks

   • Susanne Toepfer, Reinhard Guthke, Dominik Driesch, Dirk Woetzel, Michael Pfaff

   Pages 119-130

10. On the Neuronal Morphology-Function Relationship: A Synthetic Approach

    • Ben Torben-Nielsen, Karl Tuyls, Eric O. Postma

    Pages 131-144

11. Analyzing Stigmergetic Algorithms Through Automata Games

    • Peter Vrancx, Katja Verbeeck, Ann Nowé

    Pages 145-156

12. The Identification of Dynamic Gene-Protein Networks

    • Ronald L. Westra, Goele Hollanders, Geert Jan Bex, Marc Gyssens, Karl Tuyls

    Pages 157-170

13. Sparse Gene Regulatory Network Identification

    • Ralf L. M. Peeters, Stef Zeemering

    Pages 171-182

14. Back Matter

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This book contains selected and revised papers of the International Symposium on Knowledge Discovery and Emergent Complexity in Bioinformatics (KDECB 2006), held at the University of Ghent, Belgium, May 10, 2006. In February 1943, the Austrian physicist Erwin Schrodi ¨ nger, one of the founding fathers of quantum mechanics, gave a series of lectures at Trinity College in Dublin titled “What Is Life? The Physical Aspect of the Living Cell and Mind. ” In these l- tures Schrodi ¨ nger stressed the fundamental differencesencountered between observing animate and inanimate matter, and advanced some, at the time, audacious hypotheses aboutthe nature andmolecularstructureof genes, some ten yearsbeforethe discoveries of Watson and Crick. Indeed, the rules of living matter, from the molecular level to the level of supraorganic ocking behavior, seem to violate the simple basic interactions found between fundamental particles as electrons and protons. It is as if the organic molecules in the cell ‘know’ that they are alive. Despite all external stochastic uct- tions and chaos, process and additive noise, this machinery has been ticking for at least 3. 8 billion years. Yet, we may safely assume that the laws that governphysicsalso steer these complex associations of synchronous and seemingly intentional dynamics in the cell.

• Alignment
• Bayesian network
• bioinformatics
• complexity
• data mining
• knowledge discovery
• learning
• machine learning
• modeling