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

机译：长期非编码RNA蛋白质相互作用预测半监督学习的最新进展：综述

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摘要

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.
机译：近年来，越来越多的证据表明，长期的非编码RNA（LNCRNA）在复杂生物过程的发展中起着重要作用，特别是在RNA进展，染色质修饰和细胞分化以及许多其他方法中。令人惊讶的是，LNCRNA与癌症如诸如人类疾病的不可分割的关系。因此，只有通过了解更多关于LNCRNA的功能，我们可以更好地解决人类疾病的问题。然而，LNCRNA需要与蛋白质结合以进行其生物医学功能。因此，我们可以通过研究LNCRNA和蛋白质之间的关系来揭示LNCRNA功能。但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蛋白质相互作用。在这篇综述中，我们在过去两年中总结了LNCRNA蛋白质相互作用预测基础的几种计算模型，并简要介绍了他们的优势和缺点。最后，指出了LNCRNA蛋白相互作用预测的未来研究方向。

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