Neighbor-favoring weight reinforcement to improve random walk-based disease gene prioritization
Neighbor-favoring weight reinforcement to improve random walk-based disease gene prioritization
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DOI：
10.1016/j.compbiolchem.2013.01.001
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发表时间：
2013-06-01
影响因子：
3.1
通讯作者：
Kwon, Yung-Keun
中科院分区：
文献类型：
--
作者：
Le, Duc-Hau;Kwon, Yung-Keun
背景：寻找与疾病相关的候选基因是生物医学研究中的一个重要问题。最近，已经提出了许多基于网络的方法，隐含地利用模块性原则，即引起相同或相似疾病的基因倾向于形成基因/蛋白质关系网络中的物理或功能模块。在这些方法中，重启随机游走（RWR）算法被认为是最先进的方法，但传统的RWR方法没有充分考虑模块性原则。因此，我们提出了一种新的方法，称为ORIENT（邻居有利于权重强化），以提高RWR的性能，通过适当的强化的相互作用的权重接近已知的疾病genes.Results：通过广泛的模拟超过数百种疾病，我们观察到，我们的方法比传统的RWR算法表现更好。特别是，当只涉及疾病基因的最近邻基因的相互作用的权重被加强时，我们的方法效果最好。有趣的是，我们的方法的性能与随机游走重新开始的概率呈负相关，而没有权重强化的RWR的性能在密集的基因/蛋白质关系网络中呈正相关。我们进一步发现，疾病基因投影子图的密度和基因/蛋白质关系网络中疾病基因之间的路径数可能是RWR性能的解释变量。最后，与其他知名的基因优先级的工具，包括奋进，ToppGene和BioGraph的比较，发现我们的方法显示出显着更好的performance.Conclusion：两者合计，这些研究结果提供了洞察力，有效地指导RWR在疾病基因优先级。(C)2013爱思唯尔有限公司保留所有权利。
Background: Finding candidate genes associated with a disease is an important issue in biomedical research. Recently, many network-based methods have been proposed that implicitly utilize the modularity principle, which states that genes causing the same or similar diseases tend to form physical or functional modules in gene/protein relationship networks. Of these methods, the random walk with restart (RWR) algorithm is considered to be a state-of-the-art approach, but the modularity principle has not been fully considered in traditional RWR approaches. Therefore, we propose a novel method called ORIENT (neighbor-favoring weight reinforcement) to improve the performance of RWR through proper intensification of the weights of interactions close to the known disease genes.Results: Through extensive simulations over hundreds of diseases, we observed that our approach performs better than the traditional RWR algorithm. In particular, our method worked best when the weights of interactions involving only the nearest neighbor genes of the disease genes were intensified. Interestingly, the performance of our approach was negatively related to the probability with which the random walk will restart, whereas the performance of RWR without the weight-reinforcement was positively related in dense gene/protein relationship networks. We further found that the density of the disease gene-projected sub-graph and the number of paths between the disease genes in a gene/protein relationship network may be explanatory variables for the RWR performance. Finally, a comparison with other well-known gene prioritization tools including Endeavour, ToppGene, and BioGraph, revealed that our approach shows significantly better performance.Conclusion: Taken together, these findings provide insight to efficiently guide RWR in disease gene prioritization. (C) 2013 Elsevier Ltd. All rights reserved.