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首页> 外文期刊>Journal of computational biology: A journal of computational molecular cell biology >Vavien: An Algorithm for Prioritizing Candidate Disease Genes Based on Topological Similarity of Proteins in Interaction Networks
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Vavien: An Algorithm for Prioritizing Candidate Disease Genes Based on Topological Similarity of Proteins in Interaction Networks

机译：Vavien：一种基于相互作用网络中蛋白质拓扑相似性的候选疾病基因优先排序算法

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摘要

Genome-wide linkage and association studies have demonstrated promise in identifying genetic factors that influence health and disease. An important challenge is to narrow down the set of candidate genes that are implicated by these analyses. Protein-protein interaction (PPI) networks are useful in extracting the functional relationships between known disease and candidate genes, based on the principle that products of genes implicated in similar diseases are likely to exhibit significant connectivity/proximity. Information flow-based methods are shown to be very effective in prioritizing candidate disease genes. In this article, we utilize the topology of PPI networks to infer functional information in the context of disease association. Our approach is based on the assumption that PPI networks are organized into recurrent schemes that underlie the mechanisms of cooperation among different proteins. We hypothesize that proteins associated with similar diseases would exhibit similar topological characteristics in PPI networks. Utilizing the location of a protein in the network with respect to other proteins (i.e., the "topological profile" of the proteins), we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. We test the resulting algorithm, Vavien, via systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance in Man (OMIM) database. Vavien outperforms other network-based prioritization algorithms as shown in the results and is available at www.diseasegenes.org.
机译：全基因组的连锁和关联研究已证明在鉴定影响健康和疾病的遗传因素方面有希望。一个重要的挑战是缩小与这些分析有关的候选基因的范围。蛋白质-蛋白质相互作用（PPI）网络可用于提取已知疾病与候选基因之间的功能关系，其依据是涉及相似疾病的基因产物可能表现出显着的连通性/邻近性。事实表明，基于信息流的方法在优先选择候选疾病基因方面非常有效。在本文中，我们利用PPI网络的拓扑来推断疾病关联中的功能信息。我们的方法基于以下假设：PPI网络被组织为循环计划，该计划是不同蛋白质之间合作机制的基础。我们假设与相似疾病相关的蛋白质在PPI网络中将表现出相似的拓扑特征。利用蛋白质相对于其他蛋白质在网络中的位置（即蛋白质的“拓扑结构”），我们开发了一种新颖的方法来评估PPI网络中蛋白质的拓扑相似性。然后，我们使用此方法根据候选疾病基因产物与已知疾病基因产物的拓扑相似性来确定其优先级。我们通过使用集成的人PPI网络和在线孟德尔在线继承（OMIM）数据库进行系统的实验研究，测试了所得算法Vavien。结果显示，Vavien优于其他基于网络的优先级排序算法，可从www.diseasegenes.org获得。

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