标题： 基于变量数量多于观测值的数据学习LiNGAM 显示英文标题

标题： Learning LiNGAM based on data with more variables than observations

摘要： 在系统生物学中，一个非常重要的主题是开发统计方法，这些方法能够自动在基因调控网络中找到因果关系，而无需事先了解因果连接。 许多方法已经用于时间序列数据。 然而，基于稳态数据的发现方法通常是必要且更优的，因为对于许多生物系统来说，获取时间序列数据可能更加昂贵和/或不可行。 传统的方法是因果贝叶斯网络。 然而，贝叶斯网络的估计是病态的。 在许多情况下，它无法唯一识别潜在的因果网络，只能给出一大类等价的因果网络，这些网络无法根据数据分布进行区分。 我们提出了一种新的发现算法，以唯一识别基因的潜在因果网络。 据我们所知，所提出的方法是第一个基于完全可识别的因果模型称为LiNGAM的学习基因网络的算法。 我们在这里使用人工生成的数据与竞争算法进行比较，尽管用真实的微阵列基因表达数据来测试它显然更好。 显示英文摘要

摘要： A very important topic in systems biology is developing statistical methods that automatically find causal relations in gene regulatory networks with no prior knowledge of causal connectivity. Many methods have been developed for time series data. However, discovery methods based on steady-state data are often necessary and preferable since obtaining time series data can be more expensive and/or infeasible for many biological systems. A conventional approach is causal Bayesian networks. However, estimation of Bayesian networks is ill-posed. In many cases it cannot uniquely identify the underlying causal network and only gives a large class of equivalent causal networks that cannot be distinguished between based on the data distribution. We propose a new discovery algorithm for uniquely identifying the underlying causal network of genes. To the best of our knowledge, the proposed method is the first algorithm for learning gene networks based on a fully identifiable causal model called LiNGAM. We here compare our algorithm with competing algorithms using artificially-generated data, although it is definitely better to test it based on real microarray gene expression data.