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A Network Based Method for Analysis of lncRNA-Disease Associations and Prediction of lncRNAs Implicated in Diseases

机译:基于网络的lncRNA-疾病关联分析和疾病相关lncRNA预测方法

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摘要

Increasing evidence has indicated that long non-coding RNAs (lncRNAs) are implicated in and associated with many complex human diseases. Despite of the accumulation of lncRNA-disease associations, only a few studies had studied the roles of these associations in pathogenesis. In this paper, we investigated lncRNA-disease associations from a network view to understand the contribution of these lncRNAs to complex diseases. Specifically, we studied both the properties of the diseases in which the lncRNAs were implicated, and that of the lncRNAs associated with complex diseases. Regarding the fact that protein coding genes and lncRNAs are involved in human diseases, we constructed a coding-non-coding gene-disease bipartite network based on known associations between diseases and disease-causing genes. We then applied a propagation algorithm to uncover the hidden lncRNA-disease associations in this network. The algorithm was evaluated by leave-one-out cross validation on 103 diseases in which at least two genes were known to be involved, and achieved an AUC of 0.7881. Our algorithm successfully predicted 768 potential lncRNA-disease associations between 66 lncRNAs and 193 diseases. Furthermore, our results for Alzheimer's disease, pancreatic cancer, and gastric cancer were verified by other independent studies.
机译:越来越多的证据表明,长的非编码RNA(lncRNA)与许多复杂的人类疾病有关,并与之相关。尽管lncRNA-疾病协会的积累,只有很少的研究研究了这些协会在发病机制中的作用。在本文中,我们从网络角度研究了lncRNA-疾病关联,以了解这些lncRNA对复杂疾病的贡献。具体而言,我们研究了涉及lncRNA的疾病的性质以及与复杂疾病相关的lncRNA的性质。关于蛋白质编码基因和lncRNAs参与人类疾病的事实,我们基于疾病和致病基因之间的已知关联构建了编码非编码基因-疾病二分网络。然后,我们应用了一种传播算法来发现该网络中隐藏的lncRNA-疾病关联。通过对103种疾病的留一法交叉验证对算法进行了评估,其中至少涉及两个基因,并且AUC为0.7881。我们的算法成功预测了66个lncRNA与193种疾病之间的768个潜在的lncRNA疾病关联。此外,其他独立研究也证实了我们对阿尔茨海默氏病,胰腺癌和胃癌的研究结果。

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