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预测circRNA-miRNA相互作用利用基于变压器的RNA序列学习和高阶接近性保存嵌入.
Jiren Zhou1,2, Xinfei Wang3, Rui Niu1,2
1School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
iScience
|January 11, 2024
概括
我们开发了SPBCMI,这是一种用于预测循环RNA (circRNA) 和微RNA (miRNA) 相互作用的新方法. SPBCMI实现了最先进的性能,为理解这些关键的生物关系提供了更易于解释的方法.
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.
- 基因组学就是基因组学.
背景情况:
- 循环RNAs (circRNAs) 通过海绵式微RNAs (miRNAs) 调节基因表达,使它们的相互作用对医学研究至关重要.
- 目前面临的挑战包括用于模型训练的经过验证的circRNA-miRNA相互作用的稀缺性以及现有的预测模型中缺乏可解释性.
研究的目的:
- 开发一种可解释和高性能的方法来预测circRNA-miRNA相互作用.
- 解决小型训练数据集的局限性,提高circRNA-miRNA相互作用预测中的模型透明度.
主要方法:
- 提出了SPBCMI,该方法整合了来自变压器的双向编码器表示 (BERT) 的序列特征和来自图形嵌入的结构特征.
- 使用渐变增强决策树 (GBDT) 分类器来预测circRNA-miRNA相互作用.
- 使用曲线下面面积 (AUC) 和预测已知的相互作用的案例研究来评估模型性能.
主要成果:
- 在circRNA-miRNA相互作用预测方面获得了0.9143的最先进的AUC.
- 在一个案例研究中,SPBCMI准确预测了10个circRNA-miRNA相互作用中的7个.
- 与现有方法相比,在预测这些相互作用方面表现优越.
结论:
- SPBCMI提供了一种创新和高性能解决方案,用于预测circRNA-miRNA相互作用.
- 该方法增强了对circRNA-miRNA调节机制的理解.
- SPBCMI为涉及circRNA和miRNA的医学研究提供了有价值的工具.
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