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基于潜在结合部位的序列和结构信息来预测lncRNA-miRNA相互作用
Danyang Qi1, Chengyan Wu2, Zhihong Hao2
1School of Physical Science and Technology, Key Laboratory of Magnetism and Magnetic Materials for Higher Education in Inner Mongolia Autonomous Region, Baotou Teachers' College, Baotou, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
International journal of biological macromolecules
|March 19, 2025
概括
结合点信息可以改善长非编码RNA (lncRNA) 和微RNA (miRNA) 相互作用的预测. 我们的机器学习方法,BSILMI,专注于结合部位特征,以便更准确的RNA相互作用分析.
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:
- 长非编码RNAs (lncRNAs) 通过海绵式微RNAs (miRNAs) 调节基因表达.
- 目前的lncRNA-miRNA相互作用预测方法经常因依赖全长序列而遭受信息冗余.
研究的目的:
- 开发一种机器学习方法,使用结合位信息来预测lncRNA-miRNA相互作用.
- 为了提高lncRNA-miRNA相互作用预测的准确性和可靠性.
主要方法:
- 提出了BSILMI,一种利用XGBoost的机器学习方法.
- 专注于潜在结合点的序列和结构信息,包括结合的自由能量,结合点得分和RNA折叠的未配对概率.
- 开发了一个新的负采样框架,通过序列相似性对齐消除了潜在的相互作用对.
主要成果:
- 与最先进的LncMirNet方法相比,BSILMI表现出更高的性能.
- 新的负采样框架提高了预测数据集的可靠性.
- SHAP特征重要性分析确定了影响预测准确性的关键因素.
结论:
- 结合点信息对于准确预测lncRNA-miRNA相互作用至关重要.
- 这项研究为RNA相互作用的机制提供了新的见解.
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