Pe'er Dana
Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
Sci STKE. 2005 Apr 26;2005(281):pl4. doi: 10.1126/stke.2812005pl4.
High-throughput proteomic data can be used to reveal the connectivity of signaling networks and the influences between signaling molecules. We present a primer on the use of Bayesian networks for this task. Bayesian networks have been successfully used to derive causal influences among biological signaling molecules (for example, in the analysis of intracellular multicolor flow cytometry). We discuss ways to automatically derive a Bayesian network model from proteomic data and to interpret the resulting model.
高通量蛋白质组学数据可用于揭示信号网络的连通性以及信号分子之间的影响。我们提供了一份关于使用贝叶斯网络完成此任务的入门指南。贝叶斯网络已成功用于推导生物信号分子之间的因果影响（例如，在细胞内多色流式细胞术分析中）。我们讨论了从蛋白质组学数据自动推导贝叶斯网络模型并解释所得模型的方法。
