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Bridging in vivo and in vitro data from Japanese Toxicogenomics Project using network analyses

机译:使用网络分析桥接日本毒物基因组计划的体内和体外数据

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Since experiments involving animal models are labor and time intensive, there is an attempt to replace these measurements on animal models with in vitro assays which has higher acceptance in the population concerning ethical issues. In this work, we explore to what extend animal models can be replaced by in vitro assays in the context of a toxicogenomics study. The data from the Japanese Toxicogenomics Project are gene expression profiles measured by microarrays from both in vitro and animal samples. We apply a comprehensive genomic association network analysis in order to study the comparative behavior of the genomic networks for the in vivo vs. in vitro data. The genomic networks are computed based on association scores of gene-gene pairs using a partial least squares modeling of gene expression values adjusted for sacrifice time and dosage. We apply permutation based statistical tests to compare the connectivity of a given gene, as well as a class of genes in the two networks which may be affected by a given drug. The goal is to identify parts of these networks including key genes that are not significantly altered for in vivo vs. in vitro samples for the majority of the drugs.
机译:由于涉及动物模型的实验是费时费力的,因此尝试用体外测定法代替动物模型上的这些测量值,该测定法在伦理问题上在人群中具有较高的接受度。在这项工作中,我们探讨了在毒理基因组学研究的背景下,可以通过体外测定替代哪些扩展的动物模型。日本毒物基因组计划的数据是通过微阵列从体外和动物样品中测得的基因表达谱。为了研究体内和体外数据的基因组网络的比较行为,我们应用了全面的基因组关联网络分析。使用针对牺牲时间和剂量调整的基因表达值的偏最小二乘模型,基于基因-基因对的关联分数计算基因组网络。我们应用基于排列的统计测试来比较给定基因以及两个网络中可能受给定药物影响的一类基因的连通性。目的是确定这些网络的各个部分,包括对于大多数药物而言,在体内和体外样品中未显着改变的关键基因。

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