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Analysis of Large-Scale Genetic Perturbation with Linear Regression of Microarray and Bayesian Networks


This paper aims to examine how large-scale genetic perturbations reveal regulatory network and an abundance of gene-specific repressors by analyzing data from a published paper (Kemmeren et al., 2014) . The main goal is to uniformly determine the effect of different components on the expression of all other genes. The idea of their experiment is doing gene deletion of one-quarter of yeast genes individually and then observing the mRNA expression genomewide. Then genetic perturbation would be resulted, which also shows some properties including the architecture of protein complexes and pathways, identification of expression changes compatible with viability, and the varying responsiveness to genetic perturbation. And all data collected from this experiment is constructed as a genetic perturbation network which present a varying connectivities among regulators. Finally it provides a regulation network with analysis result from R package limma and sparsebn.

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