Lecture Notes in Computer Science, Volume 7833, Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics: 11th European Conference, EvoBIO 2013, Vienna, Austria, April 3-5, 2013. Proceedings

Lecture Notes in Computer Science, Volume 7833, Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics: 11th European Conference, EvoBIO 2013, Vienna, Austria, April 3-5, 2013. Proceedings

Language: English

Pages: 226

ISBN: 2:00316253

Format: PDF / Kindle (mobi) / ePub


This book constitutes the refereed proceedings of the 11th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2013, held in Vienna, Austria, in April 2013, colocated with the Evo* 2013 events EuroGP, EvoCOP, EvoMUSART and EvoApplications. The 10 revised full papers presented together with 9 poster papers were carefully reviewed and selected from numerous submissions. The papers cover a wide range of topics in the field of biological data analysis and computational biology. They address important problems in biology, from the molecular and genomic dimension to the individual and population level, often drawing inspiration from biological systems in oder to produce solutions to biological problems.

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Spine) is also connected with breast cancer (not shown here). It is well established that the estrogen receptor alpha (ESR1) drives oncogenesis in over two-thirds of all breast cancers [17]. Mutations in the estrogen receptor gene have also been associated with loss of bone mineral density in humans [20]. The HDN also reveals connections between behavioral traits and diseases. For instance, lung cancer is connected with smoking behavior and nicotine dependence within the same module (Fig. 5B).

. . . . . . . . . . . . . . . . . . . . . . . Jie Tan, Gavin D. Grant, Michael L. Whitfield, and Casey S. Greene 11 Inferring Human Phenotype Networks from Genome-Wide Genetic Associations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christian Darabos, Kinjal Desai, Richard Cowper-Sal.lari, Mario Giacobini, Britney E. Graham, Mathieu Lupien, and Jason H. Moore 23 Knowledge-Constrained K-Medoids Clustering of Regulatory Rare Alleles.

Or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in ist current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that.

Results of the evolutionary process and are located at the leaves of the tree. Hypothetical ancestors are represented by internal nodes, and ancestor-descendant relationships are modelled by branches. In the literature we can find a variety of optimality criteria for inferring phylogenies, such as maximum parsimony, maximum likelihood and distance methods [3]. However, by using a specific criterion, the resulting phylogenies can be radically different to the trees generated by other criteria,.

Fuzzy and Intelligent Systems (2007) 9. Jain, B.J., Wysotzki, F.: Efficient Pattern Discrimination with Inhibitory WTA Nets. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 827–834. Springer, Heidelberg (2001) 10. Jung, J.-Y., Reggia, J.A.: The Automated Design of Artificial Neural Networks Using Evolutionary Computation. In: Yang, A., Shan, Y., Bui, L.T. (eds.) Success in Evolutionary Computation. SCI, vol. 92, pp. 19–41. Springer, Heidelberg (2008) 11. Khan, M.,.

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