Development of Causal Structure Mining Method for Large Scale Dimensional Data and Construction of Gene Function Knowledge Base

科学家试图利用多个基因表达变量的测量数据（大尺度数据），通过分析基因表达之间的因果关系，找出每个基因的功能。然而，对数十个或数百个变量之间的因果关系的分析很难手工进行。尽管存在这个问题，基于计算机的因果分析适用的变量数量在目前的状态下被限制在20-30。因此，本工作发展了一种新的统计因果分析原理，并进一步利用我们开发的方法为科学家构建了表达基因之间功能关系的知识库。

Scientists attempt to figure out function of each gene through the analysis of causal relations between gene expressions by using measurement data of the many gene expression variables (large scale dimensional data). However, the analysis of causal relations between dozens or hundreds of variables is hardly performed manually. In spite of this problem, the number of variables to which the computer based causal analysis is applicable is limited to 20-30 in the state of the art. Accordingly, this work developed a novel principle of the statistical causal analysis, and furthermore constructed a knowledge base of the functional relations among expressed genes for the scientists by using our developed approach.