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Titlebook: Gene Regulatory Networks; Methods and Protocol Guido Sanguinetti,Vân Anh Huynh-Thu Book 2019 Springer Science+Business Media, LLC, part of

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发表于 2025-3-21 17:44:56 | 显示全部楼层 |阅读模式
书目名称Gene Regulatory Networks
副标题Methods and Protocol
编辑Guido Sanguinetti,Vân Anh Huynh-Thu
视频video
概述Includes cutting-edge methods and protocols.Provides step-by-step detail essential for reproducible results.Contains key notes and implementation advice from the experts
丛书名称Methods in Molecular Biology
图书封面Titlebook: Gene Regulatory Networks; Methods and Protocol Guido Sanguinetti,Vân Anh Huynh-Thu Book 2019 Springer Science+Business Media, LLC, part of
描述This volume explores recent techniques for the computational inference of gene regulatory networks (GRNs). The chapters in this book cover topics such as methods to infer GRNs from time-varying data; the extraction of causal information from biological data; GRN inference from multiple heterogeneous data sets; non-parametric and hybrid statistical methods; the joint inference of differential networks; and mechanistic models of gene regulation dynamics. Written in the highly successful .Methods in Molecular Biology. series format, chapters include introductions to their respective topics, descriptions of recently developed methods for GRN inference, applications of these methods on real and/ or simulated biological data, and step-by-step tutorials on the usage of associated software tools..Cutting-edge and thorough, .Gene Regulatory Networks: Methods and Protocols. is an essential tool for evaluating the current research needed to further addressthe common challenges faced by specialists in this field..
出版日期Book 2019
关键词Bayesian networks; Gaussian processes; data simulation; time series expression; single-cell transcriptom
版次1
doihttps://doi.org/10.1007/978-1-4939-8882-2
isbn_ebook978-1-4939-8882-2Series ISSN 1064-3745 Series E-ISSN 1940-6029
issn_series 1064-3745
copyrightSpringer Science+Business Media, LLC, part of Springer Nature 2019
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https://doi.org/10.1007/978-3-642-51994-9up-Lasso penalty in order to select interactions which operate both at the proteomic and at the transcriptomic level between two genes. We end up with a . network embedding information shared at multiple scales of the cell. We illustrate this method on two breast cancer data sets. An .-package is pu
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Folsäure und Ursodesoxycholsäureby combining gene expression data with protein–protein interaction networks and proteomic datasets. We conclude with a section on practical applications of a network inference algorithm to infer a genome-scale regulatory network.
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Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks,by combining gene expression data with protein–protein interaction networks and proteomic datasets. We conclude with a section on practical applications of a network inference algorithm to infer a genome-scale regulatory network.
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