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Titlebook: Mapping Genetic Interactions; Franco Joseph Vizeacoumar,Andrew Freywald Book 2021 The Editor(s) (if applicable) and The Author(s), under e

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書目名稱Mapping Genetic Interactions
編輯Franco Joseph Vizeacoumar,Andrew Freywald
視頻videohttp://file.papertrans.cn/624/623763/623763.mp4
概述Includes cutting-edge methods and protocols.Provides step-by-step detail essential for reproducible result.Contains key notes and implementation advice from the experts
叢書名稱Methods in Molecular Biology
圖書封面Titlebook: Mapping Genetic Interactions;  Franco Joseph Vizeacoumar,Andrew Freywald Book 2021 The Editor(s) (if applicable) and The Author(s), under e
描述.This volume details methods of identifying synthetic lethal, genetic interactions by various approaches in different model systems including human cancer cells. Chapters guide readers through genetic interactions in model organisms, RNA interference, CRISPR/Cas9 based genome editing technologies, drug-gene interactions, mapping chemical genetic interactions, synergistic drug-gene relations, single cell sequencing, gene expression profiling, and novel genetic interactions. Written in the format of the highly successful?.Methods in Molecular Biology?.series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols...?..Authoritative and cutting-edge, .Genetic Interaction Mapping .aims to be a useful practical guide to researches to help further their study in this field..
出版日期Book 2021
關(guān)鍵詞lower eukaryotes; synthetic genetic array (SGA); ; D; melanogaster; C; elegans; CRISPR
版次1
doihttps://doi.org/10.1007/978-1-0716-1740-3
isbn_softcover978-1-0716-1742-7
isbn_ebook978-1-0716-1740-3Series ISSN 1064-3745 Series E-ISSN 1940-6029
issn_series 1064-3745
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Busines
The information of publication is updating

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Book 2021ncer cells. Chapters guide readers through genetic interactions in model organisms, RNA interference, CRISPR/Cas9 based genome editing technologies, drug-gene interactions, mapping chemical genetic interactions, synergistic drug-gene relations, single cell sequencing, gene expression profiling, and
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Machine Learning to Identify Gene Interactions from High-Throughput Mutant Crossesres of single gene mutants to identify gene interactions in double mutants. Here we present how machine learning models that consider the characteristics of the phenotypic data improve on the classical multiplicative model. Importantly, machine learning improves the selection of cutoff values to identify gene interactions from phenotypic scores.
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