Biomolecules, such as microRNAs, circRNAs, lncRNAs and genes, are functionally interdependent in human cells, and all play critical roles in diverse fundamental and vital biological processes. The dysregulations of such biomolecules can cause diseases. Identifying the associations between biomolecules and diseases can uncover the mechanisms of complex diseases, which is conducive to their diagnosis, treatment, prognosis and prevention. Due to the time consumption and cost of biologically experimental methods, many computational association prediction methods have been proposed in the past few years. In this study, we provide a comprehensive review of machine learning-based approaches for predicting disease-biomolecule associations with multi-view data sources. Firstly, we introduce some databases and general strategies for integrating multi-view data sources in the prediction models. Then we discuss several feature representation methods for machine learning-based prediction models. Thirdly, we comprehensively review machine learning-based prediction approaches in three categories: basic machine learning methods, matrix completion-based methods and deep learning-based methods, while discussing their advantages and disadvantages. Finally, we provide some perspectives for further improving biomolecule-disease prediction methods.

中文翻译：

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预测生物分子-疾病关联的机器学习方法。

诸如microRNA，circRNA，lncRNA和基因之类的生物分子在人类细胞中在功能上相互依赖，并且在各种基本和重要的生物学过程中都起着至关重要的作用。这种生物分子的失调会引起疾病。识别生物分子与疾病之间的联系可以揭示复杂疾病的机制，这有助于它们的诊断，治疗，预后和预防。由于生物学实验方法的时间消耗和成本，在过去几年中已经提出了许多计算关联预测方法。在这项研究中，我们提供了基于机器学习的方法的全面综述，该方法用于通过多视图数据源预测疾病与生物分子的关联。首先，我们介绍了一些数据库和将多视图数据源集成到预测模型中的一般策略。然后，我们讨论了基于机器学习的预测模型的几种特征表示方法。第三，我们在三个方面全面回顾了基于机器学习的预测方法：基本机器学习方法，基于矩阵完成的方法和基于深度学习的方法，同时讨论了它们的优缺点。最后，我们为进一步改善生物分子疾病的预测方法提供了一些见解。讨论基于矩阵完成的方法和基于深度学习的方法的优缺点。最后，我们为进一步改善生物分子疾病的预测方法提供了一些见解。讨论基于矩阵完成的方法和基于深度学习的方法的优缺点。最后，我们为进一步改善生物分子疾病的预测方法提供了一些见解。