MLWNNR:LncRNA-疾病关联预测与多核学习驱动的权重核规范规范化
Guo-Bo Xie1, Hao-Jie Xu1, Guo-Sheng Gu2
1School of Computer Science, Guangdong University of Technology, Guangzhou, 510000, China.
Interdisciplinary sciences, computational life sciences
|June 23, 2025
概括
这项研究引入了一种新的算法,MLWNNR,用于预测长非编码RNA (lncRNA) 与疾病之间的联系. 该方法准确地识别了潜在的关联,有助于理解人类的病理.
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:
- 长非编码RNAs (lncRNAs) 是人类疾病的关键调节者.
- 预测lncRNA与疾病的关联对于理解疾病机制至关重要.
研究的目的:
- 开发一个强大的算法来预测 lncRNA-疾病的关联.
- 为了利用多核学习和网络完成,进行准确的预测.
主要方法:
- 利用基于k-最近邻近的内核学习算法来整合多相似性内核.
- 构建了一个异质的 lncRNA-疾病关联网络.
- 应用加权核规范规范化用于网络完成和协会评分.
主要成果:
- 与其他六种模型相比,MLWNNR算法在三个数据集上表现出卓越的性能.
- 案例研究证实了大多数预测的lncRNA疾病关联与现有文献.
- 该模型表现出强性和出色的概括能力.
结论:
- MLWNNR提供了一种可靠的计算方法来推断lncRNA与疾病的关联.
- 这种方法可以帮助识别人类疾病的新生物标志物和治疗点.
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