Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China.
Shenyuan Honors College and School of Mathematics and Systems Science, Beihang University, Beijing, 100191, China.
Sci Rep. 2019 Mar 21;9(1):5017. doi: 10.1038/s41598-019-41539-w.
The gene interaction network is one of the most important biological networks and has been studied by many researchers. The gene interaction network provides information about whether the genes in the network can cause or heal diseases. As gene-gene interaction relations are constantly explored, gene interaction networks are evolving. To describe how much a gene has been studied, an approach based on a logistic model for each gene called gene saturation has been proposed, which in most cases, satisfies non-decreasing, correlation and robustness principles. The average saturation of a group of genes can be used to assess the network constructed by these genes. Saturation reflects the distance between known gene interaction networks and the real gene interaction network in a cell. Furthermore, the saturation values of 546 disease gene networks that belong to 15 categories of diseases have been calculated. The disease gene networks' saturation for cancer is significantly higher than that of all other diseases, which means that the disease gene networks' structure for cancer has been more deeply studied than other disease. Gene saturation provides guidance for selecting an experimental subject gene, which may have a large number of unknown interactions.
基因相互作用网络是最重要的生物网络之一，已经有许多研究人员对其进行了研究。基因相互作用网络提供了有关网络中基因是否可以引起或治愈疾病的信息。随着对基因-基因相互作用关系的不断探索，基因相互作用网络也在不断发展。为了描述一个基因被研究的程度，已经提出了一种基于逻辑模型的方法，称为基因饱和，它在大多数情况下满足非递减、相关性和稳健性原则。一组基因的平均饱和度可以用来评估由这些基因构建的网络。饱和度反映了已知基因相互作用网络与细胞中真实基因相互作用网络之间的距离。此外，还计算了属于 15 种疾病类别的 546 个疾病基因网络的饱和度。癌症疾病基因网络的饱和度明显高于其他所有疾病，这意味着癌症疾病基因网络的结构比其他疾病研究得更深入。基因饱和度为选择可能有大量未知相互作用的实验对象基因提供了指导。
