Least absolute regression network analysis of the murine osteoblast differentiation network
Least absolute regression network analysis of the murine osteoblast differentiation network
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DOI：
10.1093/bioinformatics/bti816
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发表时间：
2006-02-15
期刊：
影响因子：
5.8
通讯作者：
Reinders, MJT
中科院分区：
文献类型：
--
作者：
van Someren, EP;Vaes, BLT;Reinders, MJT
动机：我们提出了一种逆向工程方案，从全基因组转录数据中发现遗传调控，监控细胞环境变化后的动态转录反应。通过同时缩小最小绝对权重和预测误差求解线性模型来估计相互作用网络。结果：所提出的方案已应用于刺激进行成骨细胞分化的小鼠C2C12细胞系。结果表明，我们的方法发现了遗传相互作用，这些相互作用显示出文献中共被引的显着丰富。更详细的研究表明，推断的网络表现出与当前生物学知识一致的属性和假设。
Motivation: We propose a reverse engineering scheme to discover genetic regulation from genome-wide transcription data that monitors the dynamic transcriptional response after a change in cellular environment. The interaction network is estimated by solving a linear model using simultaneous shrinking of the least absolute weights and the prediction error.Results: The proposed scheme has been applied to the murine C2C12 cell-line stimulated to undergo osteoblast differentiation. Results show that our method discovers genetic interactions that display significant enrichment of co-citation in literature. More detailed study showed that the inferred network exhibits properties and hypotheses that are consistent with current biological knowledge.