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LPGNMF: Predicting Long Non-Coding RNA and Protein Interaction Using Graph Regularized Nonnegative Matrix Factorization.
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 4.5 ) Pub Date : 2018-07-31 , DOI: 10.1109/tcbb.2018.2861009
Tianyi Zhang , Minghui Wang , Jianing Xi , Ao Li

Long non-coding RNAs (lncRNA) play crucial roles in a variety of biological processes and complex diseases. Massive studies have indicated that lncRNAs interact with related proteins to exert regulation of cellular biological processes. Because it is time-consuming and expensive to determine lncRNA-protein interaction by experiment, more accurate predictions of interaction by computational methods are imperative. We propose a novel computational approach, predicting lncRNA-protein interaction using graph regularized nonnegative matrix factorization (LPGNMF), to discover unobserved lncRNA-protein association. First, we calculate lncRNA similarity and protein similarity by integrating the lncRNA expression information and gene ontology information. Subsequently, we utilize graph regularized nonnegative matrix factorization framework to predict potential interactions for all lncRNA simultaneously. In the cross validation test, LPGNMF achieves an AUC of 85.2 percent, higher than those of other compared methods. In addition, novel lncRNA-protein interactions detected by LPGNMF are validated by literatures or database. The results indicate that our method is effective to discover potential lncRNA-protein interaction.

中文翻译:

LPGNMF:使用图正则化非负矩阵分解预测长的非编码RNA和蛋白质相互作用。

长的非编码RNA(lncRNA)在多种生物过程和复杂疾病中起着至关重要的作用。大量研究表明,lncRNA与相关蛋白发生相互作用,从而调节细胞生物学过程。由于通过实验确定lncRNA-蛋白质相互作用既耗时又昂贵,因此必须通过计算方法更准确地预测相互作用。我们提出了一种新的计算方法,使用图正则化非负矩阵分解(LPGNMF)预测lncRNA-蛋白质相互作用,以发现未观察到的lncRNA-蛋白质关联。首先,我们通过整合lncRNA表达信息和基因本体信息来计算lncRNA相似性和蛋白质相似性。后来,我们利用图正则化非负矩阵分解框架来同时预测所有lncRNA的潜在相互作用。在交叉验证测试中,LPGNMF的AUC达到85.2%,高于其他比较方法。此外,通过文献或数据库验证了通过LPGNMF检测到的新型lncRNA-蛋白质相互作用。结果表明,我们的方法可有效发现潜在的lncRNA-蛋白质相互作用。
更新日期:2020-03-07
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