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Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 13th International M Andrea Bracciali,Giulio Caravagna,Roberto Tag

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發(fā)表于 2025-3-21 19:53:43 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computational Intelligence Methods for Bioinformatics and Biostatistics
副標(biāo)題13th International M
編輯Andrea Bracciali,Giulio Caravagna,Roberto Tagliafe
視頻videohttp://file.papertrans.cn/233/232393/232393.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 13th International M Andrea Bracciali,Giulio Caravagna,Roberto Tag
描述.This book constitutes the thoroughly refereed post-conference proceedings of the 13th International Meeting on Computational Intelligence Methods? for Bioinformatics and Biostatistics, CIBB 2016, held in Stirling, UK, in September 2016. The 19 revised full papers and 6 keynotes abstracts presented were carefully reviewed and selected from 61 submissions. The papers? deal with the application of computational intelligence to open problems in bioinformatics, biostatistics, systems and synthetic biology, medicalinformatics, computational approaches to life sciences in general.
出版日期Conference proceedings 2017
關(guān)鍵詞bioinformatics; biostatistics; systems biology; computational modeling; stochastic models; computational
版次1
doihttps://doi.org/10.1007/978-3-319-67834-4
isbn_softcover978-3-319-67833-7
isbn_ebook978-3-319-67834-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/232393.jpg
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https://doi.org/10.1007/978-3-319-67834-4bioinformatics; biostatistics; systems biology; computational modeling; stochastic models; computational
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978-3-319-67833-7Springer International Publishing AG 2017
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Susanne Kytzia,Claude Siegenthalerxpressed genes restore the phenotype and whether, globally, the drug has an effect on the disease. We propose a method that exploits gene-expression data and network biology information to build a mediation analysis model for the evaluation of the effect of treatment on the disease at molecular leve
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https://doi.org/10.1007/978-3-642-79793-4 be used as tumor markers. However, current techniques for identifying CpG islands suffer from various drawbacks. In this paper, we propose a novel algorithm to detect CpG islands by combining clustering techniques with complementary chaotic particle swarm optimization. Clustering techniques are use
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