Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation
Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation
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DOI：
10.1093/bib/bbac595
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发表时间：
2023-01-02
影响因子：
9.5
通讯作者：
Chen, Lang-cheng
中科院分区：
文献类型：
--
作者：
xie, Guo-Bo;Chen, Rui-Bin;Chen, Lang-cheng
最近的研究表明，长链非编码rna （lncRNAs）与几种人类疾病密切相关，为其在检测和治疗中的应用提供了新的机会。许多图传播和相似融合方法可用于预测lncrna与疾病的潜在关联。然而，现有的相似融合方法在融合过程中存在噪声和自相似损失。为了解决这些问题，本文提出了一种基于选择性相似矩阵融合（SSMF）和双向线性邻域标签传播（BLNP）有机结合的预测lncrna -疾病关联的新方法，称为SSMF-BLNP。在SSMF中，lncrna与疾病的自相似网络通过选择性预处理和非线性迭代融合得到。融合过程为每个初始相似网络分配权重，并引入一个单元矩阵，该矩阵可以降低噪声并补偿自相似的损失。在BLNP中，lncRNA-疾病的初始关联在lncRNA和疾病方向上都被用作线性邻域标签传播的标签信息。然后对从SSMF中得到的自相似网络进行传播，得到预测lncrna与疾病关系的评分矩阵。实验结果表明，SSMF-BLNP比其他7种最先进的方法性能更好。此外，一项案例研究表明，与肝细胞癌相关的10种lncrna和与肾细胞癌相关的10种lncrna的准确率分别高达100%和80%。本文中使用的源代码和数据集可在https://github.com/RuiBingo/SSMF-BLNP上获得。
Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA-disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA-disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA-disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https://github.com/RuiBingo/SSMF-BLNP.