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Titlebook: Knowledge Discovery and Emergent Complexity in Bioinformatics; First International Karl Tuyls,Ronald Westra,Ann Nowé Conference proceeding

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书目名称Knowledge Discovery and Emergent Complexity in Bioinformatics
副标题First International
编辑Karl Tuyls,Ronald Westra,Ann Nowé
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Knowledge Discovery and Emergent Complexity in Bioinformatics; First International  Karl Tuyls,Ronald Westra,Ann Nowé Conference proceeding
描述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 assum
出版日期Conference proceedings 2007
关键词Alignment; Bayesian network; bioinformatics; complexity; data mining; knowledge discovery; learning; machin
版次1
doihttps://doi.org/10.1007/978-3-540-71037-0
isbn_softcover978-3-540-71036-3
isbn_ebook978-3-540-71037-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2007
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0302-9743 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 assum978-3-540-71036-3978-3-540-71037-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Ronald Westra,Karl Tuyls,Yvan Saeys,Ann Nowéize likelihoods with respect to the usual Lebesgue measure of the data space, and (2) to bound the likelihood when its exact value is unattainable. We provide practical algorithms for these ideas and illustrate their use on synthetic data, images of digits and faces, as well as signals extracted fro
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Ricardo Grau,Maria del C. Chavez,Robersy Sanchez,Eberto Morgado,Gladys Casas,Isis Bonetize likelihoods with respect to the usual Lebesgue measure of the data space, and (2) to bound the likelihood when its exact value is unattainable. We provide practical algorithms for these ideas and illustrate their use on synthetic data, images of digits and faces, as well as signals extracted fro
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Yvan Saeys,Yves Van de Peerhe determinant as a sharpness function in an autofocus algorithm. We test the method on a large database of microscopy images with given ground truth focus results. We found that for a vast majority of the focus sequences the results are in the correct focal range. Cases where the algorithm fails ar
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