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Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 11th International M Clelia DI Serio,Pietro Liò,Roberto Tagliaferr

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书目名称Computational Intelligence Methods for Bioinformatics and Biostatistics
副标题11th International M
编辑Clelia DI Serio,Pietro Liò,Roberto Tagliaferri
视频videohttp://file.papertrans.cn/233/232384/232384.mp4
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 11th International M Clelia DI Serio,Pietro Liò,Roberto Tagliaferr
描述.This book constitutes the thoroughly refereed post-conference proceedings of the 11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2014, held in Cambridge, UK, in June 2014..The 25 revised full papers presented were carefully reviewed and selected from 44 submissions. The papers focus problems concerning computational techniques in bioinformatics, systems biology, medical informatics and biostatistics..
出版日期Conference proceedings 2015
关键词evolutionary algorithms; gene ontology; image analysis; machine learning; parallel computing; association
版次1
doihttps://doi.org/10.1007/978-3-319-24462-4
isbn_softcover978-3-319-24461-7
isbn_ebook978-3-319-24462-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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Grundbegriffe der deskriptiven Statistik,mators. In the context of Gaussian graphical modeling, we compare the proposed estimator to the graphical lasso. This work is a brief exposé of the technical developments in [1], focussing on applications in gene-gene interaction network reconstruction.
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https://doi.org/10.1007/978-3-319-24462-4evolutionary algorithms; gene ontology; image analysis; machine learning; parallel computing; association
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https://doi.org/10.1007/978-3-8348-9110-5on of a biological concept that is associated to one or more gene products through a process also known as annotation. Each annotation may be derived using different methods and an Evidence Code (EC) takes into account of this process. The importance and the specificity of both GO terms and annotati
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Grundbegriffe der deskriptiven Statistik,longing to the same group are more similar between each other than items in different groups. Consensus clustering is a methodology for combining different clustering solutions from the same data set in a new clustering, in order to obtain a more accurate and stable solution. In this work we compare
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,Urnen- und Teilchen/Fächer-Modelle,xpert, are now subjected to computational analytics. The use of machine learning techniques allows one to extend the computational imaging approach by considering various markers based on DNA, mRNA, microRNA (miRNA) and proteins that could be used for classification of disease taxonomy, response to
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