Huang Yiran, Wu Zhihong, Lan Wei, Zhong Cheng
IEEE/ACM Trans Comput Biol Bioinform. 2023 Sep-Oct;20(5):3173-3181. doi: 10.1109/TCBB.2023.3284505. Epub 2023 Oct 9.
Recent studies revealed that the modification of N7-methylguanosine (mG) has associations with many human diseases. Effectively identifying disease-associated mG methylation sites would provide crucial clues for disease diagnosis and treatment. Previous studies have developed computational methods to predict disease-associated mG sites based on similarities among mG sites and diseases. However, few have focused on the influence of the known mG-disease association information on calculating similarity measures of mG site and disease, which potentially promotes the identification of the disease-associated mG sites. In this work, we propose а computational method called mGDP-RW to predict mG-disease associations by random walk algorithm. mGDP-RW first incorporates the feature information of mG site and disease with the known mG-disease associations to compute mG site similarity and disease similarity. Then mGDP-RW combines the known mG-disease associations with the computed similarity of mG site and disease to construct a mG-disease heterogeneous network. Finally, mGDP-RW utilizes a two-pass random walk with restart algorithm to find novel mG-disease associations on the heterogeneous network. The experimental results show that our method achieves higher prediction accuracy compared to the existing methods. The study case also demonstrates the effectiveness of mGDP-RW in discovering potential mG-disease associations.
最近的研究表明，N7-甲基鸟苷（mG）修饰与许多人类疾病有关。有效识别与疾病相关的mG甲基化位点将为疾病诊断和治疗提供关键线索。以往的研究已经开发出基于mG位点和疾病之间的相似性来预测与疾病相关的mG位点的计算方法。然而，很少有人关注已知的mG-疾病关联信息对计算mG位点和疾病的相似性度量的影响，这可能有助于识别与疾病相关的mG位点。在这项工作中，我们提出了一种名为mGDP-RW的计算方法，通过随机游走算法来预测mG-疾病关联。mGDP-RW首先将mG位点和疾病的特征信息与已知的mG-疾病关联相结合，以计算mG位点相似性和疾病相似性。然后，mGDP-RW将已知的mG-疾病关联与计算出的mG位点和疾病的相似性相结合，构建一个mG-疾病异质网络。最后，mGDP-RW利用带重启的两遍随机游走算法在异质网络上寻找新的mG-疾病关联。实验结果表明，与现有方法相比，我们的方法具有更高的预测准确率。研究案例也证明了mGDP-RW在发现潜在的mG-疾病关联方面的有效性。
