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Heterogeneous network embedding enabling accurate disease association predictions

机译：异质网络嵌入能够实现准确的疾病协会预测

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It is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation. We incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset. We propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation.
机译：识别生物医学研究中各种疾病的复杂生物机制是显着的。最近，在基因组学，表观症，偏心组织，蛋白质组学，代谢组科，营养学等中不断增长的数据巨大数据，导致探索复杂疾病的系统生物学手段的兴起。然而，多数据的生产与我们分析数据能力之间的差异已经逐渐扩大。此外，我们观察到，网络可以代表许多上述数据，并且在由网络嵌入方法中学到的矢量表示中创立，其密切关注但目前实际上并不具有直接链接非常可能与之相关，因此，他们是有希望的生物调查的候选人科目。我们纳入了六个公共生物数据库，以构建包含三类实体（即基因，疾病，miRNA）和多种类型的边缘（即，已知关系）的异质生物网络。为了解决固有的异构性，我们开发一个异构网络嵌入模型，用于将网络映射到一个低维矢量空间中，其中实体之间的关系良好。并且为了评估我们方法的有效性，我们进行基因疾病以及miRNA疾病关联预测，结果表明我们在几种最先进的新颖方法的优势。此外，我们的方法预测的许多关联在最新的真实数据集中验证。我们提出了一种新的异构网络嵌入方法，可以充分利用异构网络的丰富上下文信息和结构。此外，我们说明了提出的方法对生物学研究的提出方法，这可以帮助识别生物调查中的新假设。

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