从数据到知识：统一系统扰动数据的知识挖掘和数据挖掘揭示信号系统的结构
From data towards knowledge: Revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data
摘要：
遗传和药理学干扰实验，如删除基因和监测基因表达反应，是研究细胞信号转导途径的有力工具。然而，它仍然是一个挑战，自动获得知识的细胞信号系统在概念层面上从系统的扰动响应数据。在这项研究中，我们探索了一个框架，统一的知识挖掘和数据挖掘方法的目标。该框架由以下自动化过程组成：1）应用本体驱动的知识挖掘方法来识别响应于扰动的基因中的功能模块，以揭示受扰动影响的潜在信号; 2）应用基于图的数据挖掘方法来搜索影响关于功能模块的公共信号的扰动，以及3）揭示信令系统的体系结构，基于它们的关系将信令单元组织成层次结构。应用这个框架的酵母扰动响应数据的纲要，我们已经成功地恢复了许多著名的信号转导途径，此外，我们的分析导致了许多关于酵母信号转导系统的假设，最后，我们的分析自动组织扰动的基因作为一个图形，反映了建筑师的酵母信号系统。重要的是，这一框架将分子发现从基因水平转化为概念水平，这很容易转化为关于酵母信号系统的规则形式的可计算知识，例如“如果参与MAPK信号传导的基因受到干扰，参与信息素反应的基因将差异表达”。
英文摘要：
Genetic and pharmacological perturbation experiments, such as deleting a gene and monitoring gene expression responses, are powerful tools for studying cellular signal transduction pathways. However, it remains a challenge to automatically derive knowledge of a cellular signaling system at a conceptual level from systematic perturbation-response data. In this study, we explored a framework that unifies knowledge mining and data mining approaches towards the goal. The framework consists of the following automated processes: 1) applying an ontology-driven knowledge mining approach to identify functional modules among the genes responding to a perturbation in order to reveal potential signals affected by the perturbation; 2) applying a graph-based data mining approach to search for perturbations that affect a common signal with respect to a functional module, and 3) revealing the architecture of a signaling system organize signaling units into a hierarchy based on their relationships. Applying this framework to a compendium of yeast perturbation-response data, we have successfully recovered many well-known signal transduction pathways; in addition, our analysis have led to many hypotheses regarding the yeast signal transduction system; finally, our analysis automatically organized perturbed genes as a graph reflecting the architect of the yeast signaling system. Importantly, this framework transformed molecular findings from a gene level to a conceptual level, which readily can be translated into computable knowledge in the form of rules regarding the yeast signaling system, such as "if genes involved in MAPK signaling are perturbed, genes involved in pheromone responses will be differentially expressed".
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
