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Prioritizing complex disease risk genes by integrating multiple data

机译:通过整合多个数据优先考虑复杂疾病风险基因

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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.
机译:复杂的疾病,如肥胖,II型糖尿病和慢性阻塞性肺病(COPD)作为与代谢紊乱有关的疾病是在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对计算系统生物学的兴趣。本文通过将功能注释,蛋白质相互作用和基因表达信息与疾病相关代谢网络中基因之间评估基因之间的相似性,提出了一种新的方法优先考虑疾病风险基因(PDRG)。基因优先化方法是针对肥胖和COPD进行的,其有效性优于TOPPGENE和TOPPNET,在文献验证和ROOCV中的召回率。我们的方法可以广泛应用于其他新陈代谢相关的疾病,有助于优先考虑新型疾病风险基因,并且可以在诊断和有效的疗法上阐明光线。

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