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将BERT预培训与图形共同邻居集成在一起,以预测ceRNA相互作用
Zhengxing Xie1, Tianping Ying2, Ge Jing2
1Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China.
Frontiers in genetics
|September 19, 2025
概括
本研究介绍了基于BERT的ceRNA图形预测器 (BCGP),通过集成序列和图形数据,准确预测microRNA (miRNA) 与长非编码RNA (lncRNA) 和圆形RNA (circRNA) 的相互作用.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 预测与微RNA (miRNA) 竞争的内源RNA (ceRNA) 相互作用对于理解基因调节至关重要.
- 对于miRNA-ceRNA预测的现有图形神经网络 (GNN) 忽略了RNA序列信息.
研究的目的:
- 开发一种新的模型,即基于BERT的ceRNA图形预测器 (BCGP),用于增强的miRNA-ceRNA关联预测.
- 将RNA序列信息与基于图形的交互数据集成.
主要方法:
- 利用基于变压器的模型生成上下文化的RNA序列表示.
- 用序列衍生特征丰富了RNA相互作用图.
- 采用神经共同邻居 (NCN) 技术进行精致节点特征提取.
主要成果:
- 在lncRNA-miRNA和circRNA-miRNA关联预测任务上,BCGP显著优于现有的方法.
- 在预测真实数据集中的miRNA-lncRNA和miRNA-circRNA相互作用方面取得了更高的准确性.
结论:
- 将RNA序列信息与基于图的相互作用集成,可以提高miRNA-ceRNA关联预测的准确性.
- BCGP提供了一种有价值的计算工具,用于剖析复杂的基因调节网络.
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