Oncotarget

Research Papers:

LPI-NRLMF: lncRNA-protein interaction prediction by neighborhood regularized logistic matrix factorization

Hongsheng Liu, Guofei Ren, Huan Hu, Li Zhang, Haixin Ai, Wen Zhang and Qi Zhao _

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Oncotarget. 2017; 8:103975-103984. https://doi.org/10.18632/oncotarget.21934

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Abstract

Hongsheng Liu1,2,3,*, Guofei Ren4,*, Huan Hu1, Li Zhang1, Haixin Ai1, Wen Zhang5 and Qi Zhao2,6

1School of Life Science, Liaoning University, Shenyang, 110036, China

2Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China

3Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China

4School of Information, Liaoning University, Shenyang, 110036, China

5School of Computer, Wuhan University, Wuhan, 430072, China

6School of Mathematics, Liaoning University, Shenyang, 110036, China

*These authors share co-first authorship

Correspondence to:

Qi Zhao, email: [email protected]

Keywords: lncRNA; protein; interaction prediction; neighborhood regularized

Received: July 26, 2017    Accepted: August 28, 2017    Published: October 19, 2017

ABSTRACT

LncRNA-protein interactions play important roles in many important cellular processes including signaling, transcriptional regulation, and even the generation and progression of complex diseases. However, experimental methods for determining proteins bound by a specific lncRNA remain expensive, difficult and time-consuming, and only a few theoretical approaches are available for predicting potential lncRNA-protein associations. In this study, we developed a novel matrix factorization computational approach to uncover lncRNA-protein relationships, namely lncRNA-protein interactions prediction by neighborhood regularized logistic matrix factorization (LPI-NRLMF). Moreover, it is a semi-supervised and does not need negative samples. As a result, new model obtained reliable performance in the leave-one-out cross validation (the AUC of 0.9025 and AUPR of 0.6924), which significantly improved the prediction performance of previous models. Furthermore, the case study demonstrated that many lncRNA-protein interactions predicted by our method can be successfully confirmed by experiments. It is anticipated that LPI-NRLMF could serve as a useful resource for potential lncRNA-protein association identification.


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