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.

近年来，越来越多的证据表明，长的非编码RNA（lncRNA）在复杂的生物学过程的发展中起着重要作用，尤其是在RNA进展，染色质修饰和细胞分化以及许多其他过程中。出乎意料的是，lncRNA与人类疾病（例如癌症）有着密不可分的关系。因此，只有对lncRNA的功能有更多的了解，才能更好地解决人类疾病的问题。但是，lncRNA需要结合蛋白质才能执行其生物医学功能。因此，我们可以通过研究lncRNA与蛋白质之间的关系来揭示lncRNA的功能。但是由于传统实验的局限性，研究人员经常使用计算预测模型来预测lncRNA蛋白相互作用。在这篇评论中 我们总结了过去两年基于半监督学习的lncRNA蛋白相互作用预测的几种计算模型，并简要介绍了它们的优点和缺点。最后指出了lncRNA蛋白相互作用预测的未来研究方向。