DeepCMI:基于图的模型,可准确预测具有多种信息的 circRNA-miRNA 相互作用。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Yue-Chao Li, Zhu-Hong You, Chang-Qing Yu, Lei Wang, Lun Hu, Peng-Wei Hu, Yan Qiao, Xin-Fei Wang, Yu-An Huang
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引用次数: 0

摘要

最近,竞争性内源 RNA 通过 microRNAs 的相互作用在调节基因表达方面的作用与循环 RNAs(circRNAs)在繁殖和凋亡等各种生物过程中的表达密切相关。虽然已证实的环状 RNA-miRNA 相互作用(CMIs)的数量在不断增加,但传统的体外发现方法成本高、劳动强度大且耗时。因此,迫切需要通过适当的数据建模和基于已知信息的预测来有效预测潜在的 CMIs。在这项研究中,我们提出了一种名为 DeepCMI 的新型模型,该模型利用 circRNA/miRNA 的多源信息来预测潜在的 CMIs。在 CMI-9905 和 CMI-9589 数据集上进行的综合评估表明,DeepCMI 成功地推断出了潜在的 CMI。具体来说,DeepCMI 在 CMI-9905 和 CMI-9589 数据集上的 AUC 值分别达到了 90.54% 和 94.8%。这些结果表明,DeepCMI 是预测潜在 CMI 的有效模型,并有可能大大减少下游体外研究的需要。为了方便使用我们训练有素的模型和数据,我们构建了一个计算平台,可在 http://120.77.11.78/DeepCMI/ 上查阅。这项工作中使用的源代码和数据集可在 https://github.com/LiYuechao1998/DeepCMI 上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information.

Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA-miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information. In this study, we proposed a novel model, called DeepCMI, that utilizes multi-source information on circRNA/miRNA to predict potential CMIs. Comprehensive evaluations on the CMI-9905 and CMI-9589 datasets demonstrated that DeepCMI successfully infers potential CMIs. Specifically, DeepCMI achieved AUC values of 90.54% and 94.8% on the CMI-9905 and CMI-9589 datasets, respectively. These results suggest that DeepCMI is an effective model for predicting potential CMIs and has the potential to significantly reduce the need for downstream in vitro studies. To facilitate the use of our trained model and data, we have constructed a computational platform, which is available at http://120.77.11.78/DeepCMI/. The source code and datasets used in this work are available at https://github.com/LiYuechao1998/DeepCMI.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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