College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Department of Orthopedic Surgery, the Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Genomics. 2019 Jul;111(4):590-597. doi: 10.1016/j.ygeno.2018.03.014. Epub 2018 Apr 6.
Complex diseases, such as obesity, type II diabetes and chronic obstructive pulmonary disease (COPD) as metabolic disorder-related diseases are major concern for worldwide public health in the 21st century. The identification of these disease risk genes has attracted increasing interest in computational systems biology. In this paper, a novel method was proposed to prioritize disease risk genes (PDRG) by integrating functional annotations, protein interactions and gene expression information to assess similarity between genes in a disease-related metabolic network. The gene prioritization method was successfully carried out for obesity and COPD, the effectiveness of which was superior to those of ToppGene and ToppNet in both literature validation and recall rate by LOOCV. Our method could be applied broadly to other metabolism-related diseases, helping to prioritize novel disease risk genes, and could shed light on diagnosis and effective therapies.
复杂疾病，如肥胖症、2 型糖尿病和慢性阻塞性肺疾病（COPD）等代谢紊乱相关疾病，是 21 世纪全球公共卫生的主要关注点。这些疾病风险基因的鉴定引起了计算系统生物学领域的极大关注。在本文中，我们提出了一种新的方法，通过整合功能注释、蛋白质相互作用和基因表达信息，来对疾病相关代谢网络中的基因进行相似性评估，从而对疾病风险基因进行优先级排序（PDRG）。我们成功地将该基因优先级排序方法应用于肥胖症和 COPD 中，通过 LOOCV 进行文献验证和召回率评估，其有效性优于 ToppGene 和 ToppNet。我们的方法可以广泛应用于其他代谢相关疾病，有助于对新型疾病风险基因进行优先级排序，并为疾病的诊断和有效治疗提供思路。
