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Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction:A Review


Lin Zhong*, Zhong Ming, Guobo Xie, Chunlong Fan and Xue Piao   Pages 1 - 7 ( 7 )


Background: In recent years, more and more evidences indicate that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, cell differentiation, etc. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to the protein to perform its biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein.

Objective: But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions.

Conclusion: In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.


lncRNA, protein, interactions prediction, computational prediction models, semi-supervised learning


School of Mathematics, Liaoning University, Shenyang, 110036, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, School of Computer Science, Guangdong University of Technology, Guangzhou,510006, College of Computer Science, Shenyang Aerospace University, Shenyang, 110136, School of Medical Informatics, Xuzhou Medical University, Xuzhou, 221004

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