Identifying disease-related genes is of importance for understanding of molecule mechanisms of diseases, as well as diagnosis and treatment of diseases. Many computational methods have been proposed to predict disease-related genes, but how to make full use of multi-source biological data to enhance the ability of disease-gene prediction is still challenging. In this paper, we proposed a novel method for predicting disease-related genes by using fast network embedding (PrGeFNE), which can integrate multiple types of associations related to diseases and genes. Specifically, we first constructed a heterogeneous network by using phenotype-disease, disease-gene, protein-protein and gene-GO associations; and low-dimensional representation of nodes is extracted from the network by using a fast network embedding algorithm. Then, a dual-layer heterogeneous network was reconstructed by using the low-dimensional representation, and a network propagation was applied to the dual-layer heterogeneous network to predict disease-related genes. Through cross-validation and newly added-association validation, we displayed the important roles of different types of association data in enhancing the ability of disease-gene prediction, and confirmed the excellent performance of PrGeFNE by comparing to state-of-the-art algorithms. Furthermore, we developed a web tool that can facilitate researchers to search for candidate genes of different diseases predicted by PrGeFNE, along with the enrichment analysis of GO and pathway on candidate gene set. This may be useful for investigation of diseases' molecular mechanisms as well as their experimental validations. The web tool is available at http://bioinformatics.csu.edu.cn/prgefne/.

中文翻译：

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PrGeFNE：通过快速网络嵌入预测疾病相关基因

识别疾病相关基因对于了解疾病的分子机制以及疾病的诊断和治疗具有重要意义。已经提出了许多计算方法来预测疾病相关基因，但如何充分利用多源生物数据来增强疾病基因预测能力仍然具有挑战性。在本文中，我们提出了一种通过使用快速网络嵌入（PrGeFNE）来预测疾病相关基因的新方法，该方法可以整合与疾病和基因相关的多种类型的关联。具体来说，我们首先利用表型-疾病、疾病-基因、蛋白质-蛋白质和基因-GO关联构建了一个异质网络；使用快速网络嵌入算法从网络中提取节点的低维表示。然后，使用低维表示重建双层异构网络，并将网络传播应用于双层异构网络以预测疾病相关基因。通过交叉验证和新增关联验证，我们展示了不同类型关联数据在增强疾病基因预测能力方面的重要作用，并通过与最先进算法的比较证实了PrGeFNE的优异性能. 此外，我们开发了一个网络工具，可以帮助研究人员搜索 PrGeFNE 预测的不同疾病的候选基因，以及候选基因集上 GO 和通路的富集分析。这可能有助于研究疾病的分子机制及其实验验证。