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Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 11th European Confer Leonardo Vanneschi,William S. Bush,Mario

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书目名称Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
副标题11th European Confer
编辑Leonardo Vanneschi,William S. Bush,Mario Giacobini
视频video
概述State-of-the-art research.Fast-track conference proceedings.Unique visibility
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 11th European Confer Leonardo Vanneschi,William S. Bush,Mario
描述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.
出版日期Conference proceedings 2013
关键词evolutionary algorithms; genetic programming; genome; machine learning; phenotype networks; algorithm ana
版次1
doihttps://doi.org/10.1007/978-3-642-37189-9
isbn_softcover978-3-642-37188-2
isbn_ebook978-3-642-37189-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2013
The information of publication is updating

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Dominique Andolfatto,Dominique Labbéof two time series gene expression data sets showed the usefulness of dbt-Isomap for dimensionality reduction. Moreover, they highlighted the effectiveness of .-norm which appeared as the best alternative to the Euclidean metric for time series gene expression embedding.
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Vasileios Vlachos,Aristidis Bitzenisss features, select a type of classifier and optimize the classifier’s parameters for stress recognition. The classification models used were artificial neural networks (ANNs) and support vector machines (SVMs). Stress recognition rates obtained from an ANN and a SVM without a GA were 68% and 67% re
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https://doi.org/10.1057/9781137004987ractions produced by a literature mining platform, Pathway Studio. We show that the linear distribution function of expert knowledge is the most appropriate to weigh our scores when expert knowledge from literature mining is used. We find that ACO parameters significantly affect the power of the met
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