RMDGCN:基于带有注意力机制的图形卷积网络预测RNA甲基化和疾病关联
Lian Liu1, Yumeng Zhou1, Xiujuan Lei1
1School of Computer Science, Shaanxi Normal University, Xi'an, Shaanxi, China.
PLoS computational biology
|December 6, 2023
概括
本研究介绍了RMDGCN,这是一种使用图形卷积网络来预测RNA甲基化 (m1A) 和疾病关联的计算方法. RMDGCN有效地识别了潜在的联系,降低了实验成本.
科学领域:
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- RNA的修改对于细胞过程至关重要.
- N1-甲基氨酸 (m1A) 是一种关键的RNA修饰,参与基因调节和疾病.
- 实验验证m1A与疾病的关联是资源密集的,因为m1A的丰度很低.
研究的目的:
- 开发一种高效的计算方法,用于预测N1-甲基氨酸 (m1A) 与疾病之间的关联.
- 克服实验方法在识别m1A疾病联系方面的局限性.
主要方法:
- 提出了一个计算方法,RMDGCN,采用带有注意力机制的图形卷积网络.
- 使用m1A和疾病特征及其已知的关联构建了一个异质网络.
- 利用积极未标记的学习来增强积极的样本.
主要成果:
- 通过5倍交叉验证,RMDGCN在预测m1A疾病关联方面比其他方法 (AUC = 0.9892,AUPR = 0.8682) 取得了更好的表现.
- 案例研究表明,RMDGCN能够预测m1A和疾病之间的新型关系.
结论:
- RMDGCN是一种有效和高效的计算工具,用于预测RNA甲基化 (m1A) 和疾病关联.
- 该方法有可能加速RNA修饰和疾病发病的研究.
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