双平衡增强拓非编码RNA疾病三重关联在异质图中
Laiyi Fu1,2,3, Yangyi Zhou1, Hongqiang Lyu1
1School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, Shannxi, China.
Briefings in bioinformatics
|August 3, 2025
概括
我们开发了DBATNDA,这是一种用于预测非编码RNA (ncRNA) 和疾病关联的新型模型. 它准确地识别了长非编码RNA疾病和miRNA疾病链接,解决了数据不平衡,以更好地了解疾病机制.
科学领域:
- 基因组学就是基因组学.
- 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:
- 非编码RNAs (ncRNAs),包括长非编码RNAs (lncRNAs) 和microRNAs (miRNAs),在人类疾病中至关重要.
- 预测像lncRNA-disease (LDAs) 和miRNA-disease (MDAs) 这样的关联对于理解疾病机制和治疗向至关重要.
- 现有的模型在数据不平衡方面扎,在预测各种ncRNA-疾病和ncRNA-ncRNA相互作用方面缺乏特异性.
研究的目的:
- 提出双平衡增强拓非编码RNA疾病三重组协会 (DBATNDA) 模型.
- 克服现有方法在处理数据不平衡和为特定的ncRNA类型提供差异化预测方面的局限性.
- 为了能够快速,准确和具体地预测ncRNA疾病和ncRNA-ncRNA三重组协会.
主要方法:
- 构建了一个相互作用双图,包括LDAs,MDAs和lncRNA-miRNA相互作用 (LMIs).
- 实现了基于图的平衡拓增强机制,以改善节点表示和处理不平衡的数据.
- 利用节点分类方法来预测三重组的关联.
主要成果:
- 与最先进的模型相比,DBATNDA在预测ncRNA疾病和ncRNA-ncRNA三重组关联方面表现优越.
- 该模型有效地解决了与生物关联预测固有的数据不平衡问题.
- 案例研究证实了DBATNDA预测的实际意义和目标特异性.
结论:
- DBATNDA提供了一种新的双表示策略,用于同时,差异化预测各种ncRNA疾病和ncRNA-ncRNA关联.
- 该模型提高了预测这些关键生物相互作用的准确性和特异性.
- DBATNDA代表了非编码RNA研究和疾病关联研究的计算方法的重大进步.
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