Knowledge Discovery and Emergent Complexity in Bioinformatics First International Workshop, KDECB 2006, Ghent, Belgium, May 10, 2006. Revised Selected Papers /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Tuyls, Karl (Επιμελητής έκδοσης), Westra, Ronald (Επιμελητής έκδοσης), Saeys, Yvan (Επιμελητής έκδοσης), Nowé, Ann (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007.
• Σειρά: Lecture Notes in Computer Science, 4366
• Θέματα: Life sciences.; Mathematical statistics.; Data mining.; Information storage and retrieval.; Artificial intelligence.; Bioinformatics.; Biochemistry.; Life Sciences.; Biochemistry, general.; Data Mining and Knowledge Discovery.; Artificial Intelligence (incl. Robotics).; Information Storage and Retrieval.; Computational Biology/Bioinformatics.; Probability and Statistics in Computer Science.

 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. 

 Knowledge Discovery and Emergent Complexity in Bioinformatics -- Boolean Algebraic Structures of the Genetic Code: Possibilities of Applications -- Discovery of Gene Regulatory Networks in Aspergillus fumigatus -- Complexity Measures for Gene Assembly -- Learning Relations from Biomedical Corpora Using Dependency Trees -- Advancing the State of the Art in Computational Gene Prediction -- Enhancing Coding Potential Prediction for Short Sequences Using Complementary Sequence Features and Feature Selection -- The NetGenerator Algorithm: Reconstruction of Gene Regulatory Networks -- On the Neuronal Morphology-Function Relationship: A Synthetic Approach -- Analyzing Stigmergetic Algorithms Through Automata Games -- The Identification of Dynamic Gene-Protein Networks -- Sparse Gene Regulatory Network Identification.