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An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods

机译：使用网络集成和基于核的快速基因优先排序方法对疾病-基因关联进行广泛分析

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Objective: In the context of "network medicine", gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization. Materials and methods: We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions. Results: The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different "informativeness" embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further biomedical investigation. Conclusions: Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further enhance disease gene ranking results, by adopting both local and global learning strategies, able to exploit the overall topology of the network.
机译：目的：在“网络医学”的背景下，基因优先排序方法是通过利用覆盖基因之间不同类型功能关系的大量数据来发现候选疾病基因的主要工具之一。几项工作提出了整合多种数据来源以改善疾病基因优先次序的方法，但据我们所知，没有系统的研究集中于对网络整合对基因优先次序的影响进行定量评估。在本文中，我们旨在提供对不限于遗传疾病的基因-疾病关联的广泛分析，以及对不同网络整合方法进行基因优先排序的系统比较。材料和方法：我们收集了9个代表基因之间不同功能关系的不同功能网络，并通过非加权和加权网络整合方法将它们组合在一起。然后，我们通过应用经典的逐个关联感，随机游走和随机游走以及重新启动算法以及最近提出的核化得分函数，对考虑的708个医学主题词（MeSH）疾病中的每一个进行基因排序。结果：使用经典随机游走算法和最佳单一网络获得的结果在708种MeSH疾病中的曲线下平均面积（AUC）约为0.82，而核化得分函数和网络集成将平均AUC提升至约0.89。根据Wilcoxon有符号秩和检验，通过利用嵌入在不同功能网络中的不同“信息性”，加权集成在0.01的显着性水平上优于未加权的集成。对于每种MeSH疾病，我们提供排名最高的未注释候选基因，可用于进一步的生物医学研究。结论：网络集成对于提高基因优先排序方法的性能是必要的。此外，基于核化得分函数的方法可以通过采用局部和全局学习策略来进一步增强疾病基因排名结果，从而能够利用网络的整体拓扑结构。

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