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Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 5th European Confere Elena Marchiori,Jason H. Moore,Jagath C.

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书目名称Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
副标题5th European Confere
编辑Elena Marchiori,Jason H. Moore,Jagath C. Rajapakse
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 5th European Confere Elena Marchiori,Jason H. Moore,Jagath C.
出版日期Conference proceedings 2007
关键词Microarray; bioinformatics; biology; data mining; evolution; evolutionary computation; genetics; learning; m
版次1
doihttps://doi.org/10.1007/978-3-540-71783-6
isbn_softcover978-3-540-71782-9
isbn_ebook978-3-540-71783-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2007
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asets. Moreover, the optimal classification rules generated are characterized by a strong generalization capability, as shown by their accuracy in predicting the HIV protease cleavable status of peptides in out-of-sample datasets.
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Targeting Differentially Co-regulated Genes by Multiobjective and Multimodal Optimization,atures. The method makes use of multiobjective techniques to evaluate the performance of profiles, and has a multimodal approach to produce alternative descriptions of same expression target. We apply this method to probe the regulatory networks governed by the PhoP/PhoQ two-component system in the
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Modeling the Shoot Apical Meristem in ,: Parameter Estimation for Spatial Pattern Formation, such that a particular stable pattern over the SAM cell population emerges. To this end, we propose an evolutionary algorithm-based approach and investigate different ways to improve the efficiency of the search process. Preliminary results are presented for the Brusselator, a well-known reaction-d
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A Gaussian Evolutionary Method for Predicting Protein-Protein Interaction Sites,ccessful if 50% predicted area is indeed located in protein-protein interface (i.e. the specificity is more than 0.5). We believe that the optimized parameters of our method are useful for analyzing protein-protein interfaces and for interfaces prediction methods and protein-protein docking methods.
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