MicroRNAs are involved in many critical cellular activities through binding to their mRNA targets, for example, in cell proliferation, differentiation, death, growth control, and developmental timing. Prediction of microRNA targets can assist in efficient experimental investigations on the functional roles of these small noncoding RNAs. Their accurate prediction, however, remains a challenge due to the limited understanding of underlying processes in recognizing microRNA targets. In this article, we introduce an algorithm that aims at not only predicting microRNA targets accurately but also assisting in vivo experiments to understand the mechanisms of targeting. The algorithm learns a unique hypothesis for each possible mechanism of microRNA targeting. These hypotheses are utilized to build a superior target predictor and for biologically meaningful partitioning of the data set of microRNA–target duplexes. Experimentally verified features for recognizing targets that incorporated in the algorithm enable the establishment of hypotheses that can be correlated with target recognition mechanisms. Our results and analysis show that our algorithm outperforms state-of-the-art data-driven approaches such as deep learning models and machine learning algorithms and rule-based methods for instance miRanda and RNAhybrid. In addition, feature selection on the partitions, provided by our algorithm, confirms that the partitioning mechanism is closely related to biological mechanisms of microRNA targeting. The resulting data partitions can potentially be used for in vivo experiments to aid in the discovery of the targeting mechanisms.

中文翻译：

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通过多假设学习进行人类 MicroRNA 目标预测


MicroRNA 通过与其 mRNA 靶标结合参与许多关键的细胞活动，例如细胞增殖、分化、死亡、生长控制和发育计时。 microRNA 靶标的预测有助于对这些小非编码 RNA 的功能作用进行有效的实验研究。然而，由于对识别 microRNA 靶点的基本过程了解有限，他们的准确预测仍然是一个挑战。在本文中，我们介绍了一种算法，该算法不仅旨在准确预测 microRNA 靶点，而且还有助于体内实验了解靶向机制。该算法为每种可能的 microRNA 靶向机制学习独特的假设。这些假设用于构建卓越的目标预测因子，并对 microRNA-目标双链体数据集进行具有生物学意义的划分。算法中包含的用于识别目标的经过实验验证的特征使得能够建立与目标识别机制相关的假设。我们的结果和分析表明，我们的算法优于最先进的数据驱动方法，例如深度学习模型和机器学习算法以及基于规则的方法，例如 miRanda 和 RNAhybrid。此外，我们的算法提供的分区特征选择证实了分区机制与 microRNA 靶向的生物学机制密切相关。由此产生的数据分区可用于体内实验，以帮助发现靶向机制。