LPI-ETSLP: lncRNA–protein interaction prediction using eigenvalue transformation-based semi-supervised link prediction,Molecular BioSystems

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LPI-ETSLP: lncRNA–protein interaction prediction using eigenvalue transformation-based semi-supervised link prediction
Molecular BioSystems Pub Date : 2017-06-22 00:00:00 , DOI: 10.1039/c7mb00290d
Huan Hu _{1,

2,

3,

4} , Chunyu Zhu _{1,

2,

3,

4} , Haixin Ai _{1,

2,

3,

4} , Li Zhang _{1,

2,

3,

4} , Jian Zhao _{1,

2,

3,

4} , Qi Zhao _{2,

3,

4,

5,

6} , Hongsheng Liu _{1,

2,

3,

4,

6}

Affiliation

RNA–protein interactions are essential for understanding many important cellular processes. In particular, lncRNA–protein interactions play important roles in post-transcriptional gene regulation, such as splicing, translation, signaling and even the progression of complex diseases. However, the experimental validation of lncRNA–protein interactions remains time-consuming and expensive, and only a few theoretical approaches are available for predicting potential lncRNA–protein associations. Here, we presented eigenvalue transformation-based semi-supervised link prediction (LPI-ETSLP) to uncover the relationship between lncRNAs and proteins. Moreover, it is semi-supervised and does not need negative samples. Based on 5-fold cross validation, an AUC of 0.8876 and an AUPR of 0.6438 have demonstrated its reliable performance compared with three other computational models. Furthermore, the case study demonstrated that many lncRNA–protein interactions predicted by our method can be successfully confirmed by experiments. It is indicated that LPI-ETSLP would be a useful bioinformatics resource for biomedical research studies.

中文翻译：

LPI-ETSLP：使用基于特征值转换的半监督链接预测来预测lncRNA与蛋白质的相互作用

RNA与蛋白质的相互作用对于理解许多重要的细胞过程至关重要。特别是，lncRNA与蛋白质的相互作用在转录后基因调控中起着重要作用，例如剪接，翻译，信号传导甚至复杂疾病的进展。但是，lncRNA与蛋白质相互作用的实验验证仍然是耗时且昂贵的，并且仅有少数理论方法可用于预测潜在的lncRNA与蛋白质的关联。在这里，我们提出了基于特征值变换的半监督链接预测（LPI-ETSLP），以揭示lncRNA与蛋白质之间的关系。而且，它是半监督的，不需要负样本。基于5倍交叉验证，AUC为0.8876，AUPR为0。与其他三个计算模型相比，6438已经证明了其可靠的性能。此外，该案例研究表明，通过我们的方法预测的许多lncRNA-蛋白质相互作用可以通过实验成功确认。研究表明，LPI-ETSLP将是用于生物医学研究的有用的生物信息学资源。

更新日期：2017-08-22

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