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Titlebook: Data Mining for Systems Biology; Methods and Protocol Hiroshi Mamitsuka,Charles DeLisi,Minoru Kanehisa Book 2013 Springer Science+Business

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書目名稱Data Mining for Systems Biology
副標(biāo)題Methods and Protocol
編輯Hiroshi Mamitsuka,Charles DeLisi,Minoru Kanehisa
視頻videohttp://file.papertrans.cn/263/262956/262956.mp4
概述Aids researchers in the further development of databases,mining,and visualization systems..Provides step-by-step detail essential for reproducible results.Contains key notes and implementation advice
叢書名稱Methods in Molecular Biology
圖書封面Titlebook: Data Mining for Systems Biology; Methods and Protocol Hiroshi Mamitsuka,Charles DeLisi,Minoru Kanehisa Book 2013 Springer Science+Business
描述.The post-genomic revolution is witnessing the generation of petabytes of data annually, with deep implications ranging across evolutionary theory, developmental biology, agriculture, and disease processes. .Data Mining for Systems Biology: Methods and Protocols., surveys and demonstrates the science and technology of converting an unprecedented data deluge to new knowledge and biological insight. The volume is organized around two overlapping themes, network inference and functional inference. Written in the highly successful .Methods in Molecular Biology?. series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, and key tips on troubleshooting and avoiding known pitfalls..?.Authoritative and practical, .Data Mining for Systems Biology: Methods and Protocols. also seeks to aid researchers in the further development of databases, mining and visualization systems that are central to the paradigm altering discoveries being made with increasing frequency..
出版日期Book 2013
關(guān)鍵詞Functional Inference; Network Inference; genotype; heterogeneous datasets; metabolism; nucleic acids; phen
版次1
doihttps://doi.org/10.1007/978-1-62703-107-3
isbn_softcover978-1-4939-5912-9
isbn_ebook978-1-62703-107-3Series ISSN 1064-3745 Series E-ISSN 1940-6029
issn_series 1064-3745
copyrightSpringer Science+Business Media New York 2013
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Mining Regulatory Network Connections by Ranking Transcription Factor Target Genes Using Time Serieunderlying network. We present a technique addressing this problem through focussing on a more limited problem: inferring direct targets of a transcription factor from short expression time series. The method is based on combining Gaussian process priors and ordinary differential equation models all
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Mining Frequent Subtrees in Glycan Data Using the Rings Glycan Miner Tool, the α-closed frequent subtree algorithm to find significant subtrees from within a data set of glycan structures, or carbohydrate sugar chains. The results are returned in order of .-value, which is computed based on the probability of the reproducibility of the returned structures. There is also a
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Localization Prediction and Structure-Based In Silico Analysis of Bacterial Proteins: With Emphasis focus on β-barrel outer membrane proteins (BOMPs), describing and evaluating new tools for BOMP detection and topology prediction. Finally, we apply general protein structure prediction methods on these proteins to show that the structure of most BOMPs in . can be modeled reliably.
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