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

[ Vol. 27 , Issue. 5 ]

Author(s):

Lin Zhong, Zhong Ming, Guobo Xie, Chunlong Fan* and Xue Piao*   Pages 385 - 391 ( 7 )

Abstract:


In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. 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 proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. 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.

Keywords:

lncRNA, protein, interactions prediction, computational prediction models, semi-supervised learning, biological processes.

Affiliation:

School of Mathematics, Liaoning University, Shenyang, 110036, National Engineering Laboratory for Big Data System Computing Technology, 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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