NFMCLDA:通过网络融合和矩阵完成预测基于miRNA的lncRNA疾病关联
Yibing Ma1, Yongle Shi1, Xiang Chen1
1School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China.
Computers in biology and medicine
|April 6, 2024
概括
本研究介绍了NFMCLDA,一种计算方法,通过整合各种数据源,有效预测长非编码RNA (lncRNA) -疾病关联. 与实验方法相比,NFMCLDA提供了一个更准确,更有效的替代方法,用于识别与疾病相关的lncRNAs.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 长非编码RNAs (lncRNAs) 的失调与复杂的人类疾病有关.
- 实验性识别 lncRNA-疾病关联是资源密集的.
- 在生物信息学中需要有效的计算方法.
研究的目的:
- 开发一种新的计算方法来推断 lncRNA 与疾病的关联.
- 有效地整合多来源关联数据,以提高预测准确度.
- 为了解决 lncRNA-疾病关联矩阵中的稀疏性问题.
主要方法:
- 拟议的NFMCLDA (网络融合和矩阵完成 lncRNA-疾病协会) 方法.
- 利用miRNA信息作为三层异质网络中的过渡路径.
- 在网络预处理中使用了不平衡的随机步行.
- 应用矩阵完成用于最终的关联预测.
主要成果:
- 与现有的方法相比,NFMCLDA在预测 lncRNA-疾病关联方面表现出更高的准确性.
- 实现了0.9648 (5倍CV) 和0.9713 (10倍CV) 的高曲线下的面积 (AUC) 值.
- 通过肺癌,骨髓瘤,宫癌和结肠癌的案例研究验证了预测潜力.
结论:
- NFMCLDA有效地整合了多源数据,克服了矩阵稀疏性.
- 该方法为lncRNA与疾病的关联提供了可靠和准确的预测.
- NFMCLDA具有促进疾病研究和诊断的巨大潜力.
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