Similarity-based methods for potential human microRNA-disease association prediction.
Similarity-based methods for potential human microRNA-disease association prediction.
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DOI：
10.1186/1755-8794-6-12
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发表时间：
2013-04-09
影响因子：
2.7
通讯作者：
Zhang Z
中科院分区：
文献类型：
--
作者：
Chen H;Zhang Z
识别microRNA与疾病的关联对于理解疾病的分子机制至关重要。然而，通过实验确定microRNAs与疾病之间的联系仍然具有挑战性。同时，随着每年新的microRNA的发现，一些新的microRNA需要在没有任何已知的靶疾病关联信息的情况下揭示靶疾病。因此，microRNA与疾病关联预测的计算方法得到了广泛的研究。本文基于功能相关的microRNA往往与表型相似的疾病相关这一假设，提出了三种用于microRNA与疾病关联预测的推理方法，即基于microRNA的相似性推理(MBSI)、基于表型的相似性推理(PBSI)和基于网络一致性的推理(NetCBI)。在这三种方法中使用了全球网络相似性度量来预测新的microRNA与疾病的关联。我们对242个已知的microRNA疾病关联进行了留一交叉验证预测评估，三种方法的AUC值分别为74.83%、54.02%和80.66%。然后选择了表现最好的NetCBI方法来预测新的microRNA与疾病的关联。一些被NetCBI强烈预测的关联性被公开获取的数据库所证实，这表明了该方法的有效性。新预测的关联性被公开发布，以便于未来的研究。此外，NetCBI特别适用于靶向关联信息不可用的microRNAs的靶向疾病预测。这些令人鼓舞的结果表明，我们的方法NetCBI不仅可以为识别新的microRNA与疾病的关联提供帮助，还可以指导科学研究的生物学实验。
The identification of microRNA-disease associations is critical for understanding the molecular mechanisms of diseases. However, experimental determination of associations between microRNAs and diseases remains challenging. Meanwhile, target diseases need to be revealed for some new microRNAs without any known target disease association information as new microRNAs are discovered each year. Therefore, computational methods for microRNA-disease association prediction have gained a lot of research interest. Herein, based on the assumption that functionally related microRNAs tend to be associated with phenotypically similar diseases, three inference methods were presented for microRNA-disease association prediction, namely MBSI (microRNA-based similarity inference), PBSI (phenotype-based similarity inference) and NetCBI (network-consistency-based inference). Global network similarity measure was used in the three methods to predict new microRNA-disease associations. We tested the three methods on 242 known microRNA-disease associations by leave-one-out cross-validation for prediction evaluation, and achieved AUC values of 74.83%, 54.02% and 80.66%, respectively. The best-performed method NetCBI was then chosen for novel microRNA-disease association prediction. Some associations strongly predicted by NetCBI were confirmed by the publicly accessible databases, which indicated the usefulness of this method. The newly predicted associations were publicly released to facilitate future studies. Moreover, NetCBI was especially applicable to predicting target diseases for microRNAs whose target association information was not available. The encouraging results suggest that our method NetCBI can not only provide help in identifying novel microRNA-disease associations but also guide biological experiments for scientific research.
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