生物语言图形融合模型用于circRNA-miRNA关联预测
Lu-Xiang Guo1, Lei Wang1,2,3, Zhu-Hong You4
1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China.
Briefings in bioinformatics
|March 1, 2024
概括
这项研究介绍了BGF-CMAP,这是一种用于预测循环RNA-microRNA关联的新型计算模型. 它准确地识别了复杂的关系,为现有疾病研究方法提供了优越的替代方案.
科学领域:
- 生物信息学是一种生物信息学.
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 循环RNAs (circRNAs) 和微RNAs (miRNAs) 是人类疾病的关键调节者.
- 由于成本和劳动力,对circRNA-miRNA关联 (CMAs) 的实验验证具有挑战性.
- 目前用于CMA预测的计算方法因依赖单个数据类型而受到限制.
研究的目的:
- 开发一个先进的计算模型来预测circRNA-miRNA关联 (CMA).
- 通过整合多个数据特征来克服现有方法的局限性.
- 为了解涉及circRNAs和miRNAs的疾病机制提供可靠的工具.
主要方法:
- 拟议的BGF-CMAP模型集成梯度增强决策树,自然语言处理和图形嵌入.
- 使用Word2vec用于序列属性和图形嵌入 (LINE,GraphFactor) 进行交互行为.
- 使用广泛的实验分析和与现有计算模型的比较进行验证.
主要成果:
- 对于circRNA-miRNA关联,BGF-CMAP实现了高预测准确度 (82.90%) 和AUC (0.9075).
- 与其他现有方法相比,该模型表现出优越的性能.
- 实验验证确认了前30个预测的miRNA相关circRNA中的23个.
结论:
- BGF-CMAP提供了一个强大的,准确的计算方法来预测circRNA-miRNA关联.
- 该模型为推进分子生物学和疾病发病学研究提供了宝贵的工具.
- BGF-CMAP可以作为未来研究circRNA-miRNA相互作用的科学基础.
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