DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization.
DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization.
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DOI：
10.1186/1756-0381-4-19
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发表时间：
2011-06-24
期刊：
影响因子：
4.5
通讯作者：
Koyutürk M
中科院分区：
文献类型：
--
作者：
Erten S;Bebek G;Ewing RM;Koyutürk M
高通量分子相互作用数据已被有效地用于优先考虑与疾病相关的候选基因，这是基于与类似疾病相关的基因产物可能在蛋白质-蛋白质相互作用（PPI）网络中彼此严重相互作用的观察。然而，这些应用程序面临的一个重要挑战是 PPI 数据的不完整和嘈杂本质。基于信息流的方法通过考虑间接交互和路径的多重性在一定程度上缓解了这些问题。我们证明，现有方法可能有利于高度关联的基因，从而使优先级对 PPI 网络的偏斜度分布以及可用相互作用和疾病关联数据中的确定偏差敏感。受这一观察的启发，我们提出了几种统计调整方法，使用具有相关置信度得分的 PPI 网络来解释已知疾病和候选基因的程度分布。我们表明，所提出的方法可以检测现有方法遗漏的松散连接的疾病基因，然而，这种改进可能是以高度连接的基因出现更多假阴性为代价的。因此，我们开发了一套称为 DADA 的套件，其中包括不同的统一优先级划分方法，可有效地将现有方法与拟议的统计调整策略相结合。在线人类孟德尔遗传 (OMIM) 数据库的综合实验结果表明，DADA 在优先考虑候选疾病基因方面优于现有方法。这些结果证明了在基于网络的疾病基因优先级排序以及其他基于网络的功能推理应用中采用准确的统计模型和相关调整方法的重要性。 DADA 在 Matlab 中实现，可在 http://compbio.case.edu/dada/ 上免费获取。
High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths. We demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called DADA, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that DADA outperforms existing methods in prioritizing candidate disease genes. These results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. DADA is implemented in Matlab and is freely available at http://compbio.case.edu/dada/.
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