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. 2021 Feb 25:9:647113.
doi: 10.3389/fbioe.2021.647113. eCollection 2021.

Prediction of lncRNA-Protein Interactions via the Multiple Information Integration

Yifan Chen  1   2 , Xiangzheng Fu  1 , Zejun Li  2 , Li Peng  3 , Linlin Zhuo  4
Affiliations

Prediction of lncRNA-Protein Interactions via the Multiple Information Integration

Yifan Chen et al. Front Bioeng Biotechnol. .

Abstract

The long non-coding RNA (lncRNA)-protein interaction plays an important role in the post-transcriptional gene regulation, such as RNA splicing, translation, signaling, and the development of complex diseases. The related research on the prediction of lncRNA-protein interaction relationship is beneficial in the excavation and the discovery of the mechanism of lncRNA function and action occurrence, which are important. Traditional experimental methods for detecting lncRNA-protein interactions are expensive and time-consuming. Therefore, computational methods provide many effective strategies to deal with this problem. In recent years, most computational methods only use the information of the lncRNA-lncRNA or the protein-protein similarity and cannot fully capture all features to identify their interactions. In this paper, we propose a novel computational model for the lncRNA-protein prediction on the basis of machine learning methods. First, a feature method is proposed for representing the information of the network topological properties of lncRNA and protein interactions. The basic composition feature information and evolutionary information based on protein, the lncRNA sequence feature information, and the lncRNA expression profile information are extracted. Finally, the above feature information is fused, and the optimized feature vector is used with the recursive feature elimination algorithm. The optimized feature vectors are input to the support vector machine (SVM) model. Experimental results show that the proposed method has good effectiveness and accuracy in the lncRNA-protein interaction prediction.

Keywords: feature representation; lncRNA protein interactions; mutual information; structure analysis; support vector machine.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The overall framework of the proposed method for lncRNA–protein interactions.
Figure 2
Figure 2
ROC curves for five-fold cross-validation tests of the benchmark dataset.
Figure 3
Figure 3
AUPR curves for five-fold cross-validation tests of the benchmark dataset.
See this image and copyright information in PMC

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