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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Machine Learning to Identify Gene Interactions from High-Throughput Mutant Crossess. Detecting gene interactions in even small microbial genomes relies on measuring growth phenotypes in thousands of crossed strains followed by statistical analysis to compare single and double mutants. The preferred computational approach is to use a multiplicative model that factors phenotype sco

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Chemical–Genetic Interactions as a Means to Characterize Drug Synergy, gene–environment interactions, and more recently, into the mechanism of action of human therapeutics. The success of these studies relies, in part, on?the ability to quantify the combined effects of multifactorial biological interactions. In this review, we explore the history and rationale behind

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https://doi.org/10.1007/978-1-0716-1740-3lower eukaryotes; synthetic genetic array (SGA); ; D; melanogaster; C; elegans; CRISPR

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lial cells in vivo to determine the role of transmembrane potential in the transduction of fluid mechanical forces generated by blood flow to a biochemical response. Depolarizing all of the cells within the vascular system by injecting potassium chloride into the animal‘s bloodstream clearly is not an option,978-3-662-03422-4978-3-662-03420-0

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lial cells in vivo to determine the role of transmembrane potential in the transduction of fluid mechanical forces generated by blood flow to a biochemical response. Depolarizing all of the cells within the vascular system by injecting potassium chloride into the animal‘s bloodstream clearly is not an option,978-3-662-03422-4978-3-662-03420-0