目前,癌症、糖尿病、心脏病、高血压等复杂疾病严重危害着人类的生命和健康,这些疾病并不是由孤立的单个基因发生改变所致,而是多基因联合作用的结果。从系统生物学的角度出发,针对以往基因网络重构算法非疾病驱动的问题,提出了一种利用决策森林构建复杂疾病驱动的基因网络的算法,着重关注基因间的协同作用与复杂疾病发病机理的关系。通过对结肠癌基因芯片数据的分析,识别出66个与结肠癌显著关联的基因互作,其中大部分互作关系参与细胞增殖、细胞凋亡、细胞分化、有丝分裂和免疫等与癌症密切相关的生物过程中。该方法从基因互作和通路的角度为系统研究疾病的遗传复杂性提供了一个崭新的思路。
Complex diseases such as cancer, diabetes, cardiopathy, hypertension are very unlikely controlled by a single gene instead by the underlying complex genetic interactions. Here we focused on the relationship between the genetic interactions and the pathogenesis to developed a novel approach of system biology, which were based on decision forest to construct the disease-driven gene networks. The approach was employed to analyze a publicly available microarray data set (colon cancer) . 66 significant disease-driven genetic interactions was identified, most of which were involved in the biological processes of ceils proliferation, apoptosis, differentiation, mitogenesis and immunity.