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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Meta-Path Methods for Prioritizing Candidate Disease miRNAs
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Meta-Path Methods for Prioritizing Candidate Disease miRNAs

机译：优先考虑候选疾病miRNA的元路径方法

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摘要

MicroRNAs (miRNAs) play critical roles in regulating gene expression at post-transcriptional levels. Numerous experimental studies indicate that alterations and dysregulations in miRNAs are associated with important complex diseases, especially cancers. Predicting potential miRNA-disease association is beneficial not only to explore the pathogenesis of diseases, but also to understand biological processes. In this work, we propose two methods that can effectively predict potential miRNA-disease associations using our reconstructed miRNA and disease similarity networks, which are based on the latest experimental data. We reconstruct a miRNA functional similarity network using the following biological information: the miRNA family information, miRNA cluster information, experimentally valid miRNA-target association and disease-miRNA information. We also reconstruct a disease similarity network using disease functional information and disease semantic information. We present Katz with specific weights and Katz with machine learning, on the comprehensive heterogeneous network. These methods, which achieve corresponding AUC values of 0.897 and 0.919, exhibit performance superior to the existing methods. Comprehensive data networks and reasonable considerations guarantee the high performance of our methods. Contrary to several methods, which cannot work in such situations, the proposed methods also predict associations for diseases without any known related miRNAs. A web service for the download and prediction of relationships between diseases and miRNAs is available at http://lab.malab.cn/soft/MDPredict/.
机译：MicroRNA（miRNA）在转录后水平调控基因表达中起着关键作用。大量实验研究表明，miRNA的改变和失调与重要的复杂疾病（尤其是癌症）有关。预测潜在的miRNA-疾病关联不仅有益于探索疾病的发病机理，而且有助于理解生物学过程。在这项工作中，我们基于最新的实验数据，提出了两种可以利用我们重建的miRNA和疾病相似性网络有效预测潜在miRNA疾病关联的方法。我们使用以下生物学信息重建miRNA功能相似性网络：miRNA家族信息，miRNA簇信息，实验有效的miRNA-靶标关联和疾病-miRNA信息。我们还使用疾病功能信息和疾病语义信息重建疾病相似性网络。在综合的异构网络上，我们为Katz提供特定的权重，为Katz提供机器学习。这些方法达到相应的AUC值为0.897和0.919，其性能优于现有方法。全面的数据网络和合理的考虑因素确保了我们方法的高性能。与无法在这种情况下起作用的几种方法相反，所提出的方法还可以预测没有任何已知相关miRNA的疾病的关联。可从http://lab.malab.cn/soft/MDPredict/获得用于下载和预测疾病与miRNA之间关系的Web服务。

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