School of Software, East China Jiaotong University, Nanchang, 330013, China.
School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
BMC Bioinformatics. 2019 Jul 25;20(1):404. doi: 10.1186/s12859-019-2998-8.
It has been shown that the deregulation of miRNAs is associated with the development and progression of many human diseases. To reduce time and cost of biological experiments, a number of algorithms have been proposed for predicting miRNA-disease associations. However, the existing methods rarely investigated the cause-and-effect mechanism behind these associations, which hindered further biomedical follow-ups.
In this study, we presented a CCA-based model in which the possible molecular causes of miRNA-disease associations were comprehensively revealed by extracting correlated sets of genes and diseases based on the co-occurrence of miRNAs in target gene profiles and disease profiles. Our method directly suggested the underlying genes involved, which could be used for experimental tests and confirmation. The inference of associated diseases of a new miRNA was made by taking into account the weight vectors of the extracted sets. We extracted 60 pairs of correlated sets from 404 miRNAs with two profiles for 2796 target genes and 362 diseases. The extracted diseases could be considered as possible outcomes of miRNAs regulating the target genes which appeared in the same set, some of which were supported by independent source of information. Furthermore, we tested our method on the 404 miRNAs under the condition of 5-fold cross validations and received an AUC value of 0.84606. Finally, we extensively inferred miRNA-disease associations for 100 new miRNAs and some interesting prediction results were validated by established databases.
The encouraging results demonstrated that our method could provide a biologically relevant prediction and interpretation of associations between miRNAs and diseases, which were of great usefulness when guiding biological experiments for scientific research.
已经表明，miRNA 的失调与许多人类疾病的发生和发展有关。为了减少生物实验的时间和成本，已经提出了许多用于预测 miRNA-疾病关联的算法。然而，现有的方法很少研究这些关联背后的因果机制，这阻碍了进一步的生物医学研究。
在这项研究中，我们提出了一种基于 CCA 的模型，该模型通过基于 miRNA 在靶基因谱和疾病谱中的共出现来提取相关的基因和疾病集，全面揭示了 miRNA-疾病关联的可能分子原因。我们的方法直接提出了潜在的相关基因，可用于实验测试和验证。通过考虑提取的集的权重向量，可以对新 miRNA 的相关疾病进行推断。我们从 404 个 miRNA 中的两个图谱中提取了 60 对相关集，这些图谱针对 2796 个靶基因和 362 种疾病。提取的疾病可以被认为是 miRNA 调节靶基因的可能结果，其中一些结果得到了独立信息来源的支持。此外，我们在 5 倍交叉验证的条件下对 404 个 miRNA 进行了测试，获得了 0.84606 的 AUC 值。最后，我们广泛推断了 100 个新 miRNA 的 miRNA-疾病关联，一些有趣的预测结果通过已建立的数据库得到了验证。
令人鼓舞的结果表明，我们的方法可以为 miRNA 和疾病之间的关联提供具有生物学相关性的预测和解释，这对于指导科学研究中的生物学实验非常有用。
