基于表达及网络拓扑结构挖掘动脉粥样硬化风险疾病基因

基于功能基因组信息、网络拓扑结构信息整合分析方法，利用基因表达谱数据和蛋白质互作数据挖掘动脉粥样硬化(AS)风险疾病基因，为从基因组层面研究动脉粥样硬化提供了新的视角．经过差异表达分析，支持向量机(SVM)的机器学习方法双重筛选，可以鉴别出可信度水平较高的风险疾病基因，对于研究动脉粥样硬化疾病基因在网络中的拓扑性质，建立基因与疾病发生发展过程的联系，提供了新的思路．得到了巨噬细胞样本中59个风险疾病基因，泡沫细胞中61个风险疾病基因．这些风险基因与已知疾病基因共享大部分动脉粥样硬化病变相关生物学过程及信号通路．并应用到对其他复杂疾病致病机理的研究中．

Uncovering Atherosclerotic Risk Disease Gene Based on Expression and Network Topological Structure

The integrated analysis method based on functional genome information and network topological structure information mining atherosclerotic risk disease gene, provide a new perspective of atherosclerosis research in genome level. Through double selection, differential expression analysis and support vector machine (SVM), disease related risk gene with high confidencecould be uncovered. It is helpful for accurately distinguishing the disease genes and non-disease genes in the protein-protein interaction network, and building relationship between two type of these genes. Base on our strategy, 59 risk disease genes were exploited from macrophages sample and 61 risk disease genes from foam cell sample which shared common biological function and signal pathways with known AS disease gene. Furthermore, this method could be used to study the pathogenesis of other complex diseases in genome level.