The power of protein interaction networks for associating genes with diseases.
The power of protein interaction networks for associating genes with diseases.
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DOI：
10.1093/bioinformatics/btq076
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发表时间：
2010-04-15
期刊：
影响因子：
--
通讯作者：
Kingsford C
中科院分区：
文献类型：
--
作者：
Navlakha S;Kingsford C
研究动机：了解遗传疾病及其致病基因之间的关系是关系到人类健康的重要问题。随着最近大量描述基因产物之间相互作用的高通量数据的涌入，科学家们已经为推断这些关联提供了一条新的途径。尽管最近在这个问题上的兴趣，但是，很少有了解的相对优势和缺点的基础上提出的技术。结果如下：我们通过检查七种最近开发的计算方法（以及它们的几种变体）的性能，评估了物理蛋白质相互作用在确定基因与疾病关联方面的实用性。我们发现，随机游走方法单独优于聚类和邻域方法，尽管大多数方法的预测不是由任何其他方法。我们将展示如何将这些方法结合成一个共识的方法产生帕累托最优性能。我们还量化了疾病相关蛋白质的扩散拓扑分布如何对预测质量产生负面影响，从而能够识别特别适合基于网络的预测的疾病以及绝对需要额外信息源的疾病。可用性：所考虑的每种算法的预测可在www.example.com上在线获得carlk@cs.umd.edu人：http://www.cbcb.umd.edu/DiseaseNet补充信息：补充数据可在生物信息学在线获得。
Motivation: Understanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene products, scientists have been provided a new avenue through which these associations can be inferred. Despite the recent interest in this problem, however, there is little understanding of the relative benefits and drawbacks underlying the proposed techniques. Results: We assessed the utility of physical protein interactions for determining gene–disease associations by examining the performance of seven recently developed computational methods (plus several of their variants). We found that random-walk approaches individually outperform clustering and neighborhood approaches, although most methods make predictions not made by any other method. We show how combining these methods into a consensus method yields Pareto optimal performance. We also quantified how a diffuse topological distribution of disease-related proteins negatively affects prediction quality and are thus able to identify diseases especially amenable to network-based predictions and others for which additional information sources are absolutely required. Availability: The predictions made by each algorithm considered are available online at http://www.cbcb.umd.edu/DiseaseNet Contact: carlk@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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