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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.
Computational Prediction and Validation of an Expert’s Evaluation of Chemical Probes
