In this work, a review of predicting lncRNA-protein interactions by bioinformatics methods is provided with a focus on machine learning. Firstly, a computational framework for predicting lncRNA-protein interactions is presented. Then, the currently available data resources for the predictions have been listed. The existing methods will be reviewed by introducing their crucial steps in the prediction framework. The key functions of lncRNA, e.g., mediator on transcriptional regulation, are often involved in interacting with proteins. The interactions with proteins provide a tunnel of leveraging the molecular cooperativity for fulfilling crucial functions. Thus, the important directions in bioinformatics have been highlighted for identifying essential lncRNA-protein interactions and deciphering the dysfunctional importance of lncRNA, especially in carcinogenesis.

在这项工作中，对通过生物信息学方法预测lncRNA-蛋白质相互作用的综述提供了对机器学习的关注。首先，提出了一个预测lncRNA-蛋白质相互作用的计算框架。然后，列出了当前可用的预测数据资源。现有方法将通过在预测框架中引入关键步骤进行审查。lncRNA的关键功能（例如转录调控的介体）通常与蛋白质相互作用。与蛋白质的相互作用提供了利用分子协同作用来完成关键功能的通道。因此，生物信息学的重要方向已被突出，用于鉴定基本的lncRNA-蛋白质相互作用和破译lncRNA的功能失调重要性，