Ribonucleic acid (RNA) is a fundamental biological molecule that is essential to all living organisms, performing a versatile array of cellular tasks. The function of many RNA molecules is strongly related to the structure it adopts. As a result, great effort is being dedicated to the design of efficient algorithms that solve the folding problem: given a sequence of nucleotides, return a probable list of base pairs, referred to as the secondary structure prediction. Early algorithms have largely relied on finding the structure with minimum free energy. However, the predictions rely on effective simplified free energy models that may not correctly identify the correct structure as the one with the lowest free energy. In light of this, new, data-driven approaches that not only consider free energy, but also use machine learning techniques to learn motifs have also been investigated, and have recently been shown to outperform free energy based algorithms on several experimental data sets. In this work, we introduce the new ExpertRNA algorithm that provides a modular framework which can easily incorporate an arbitrary number of rewards (free energy or non-parametric/data driven) and secondary structure prediction algorithms. We argue that this capability of ExpertRNA has the potential to balance out different strengths and weaknesses of state-of-the-art folding tools. We test the ExpertRNA on several RNA sequence-structure data sets, and we compare the performance of ExpertRNA against a state-of-the-art folding algorithm. We find that ExpertRNA produces, on average, more accurate predictions than the structure prediction algorithm used, thus validating the promise of the approach.

中文翻译：

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ExpertRNA：RNA结构预测的新框架

核糖核酸（RNA）是基本的生物分子，对所有活生物体都是必不可少的，可以执行多种细胞任务。许多RNA分子的功能与其采用的结构密切相关。结果，人们致力于设计有效的算法来解决折叠问题：给定核苷酸序列，返回可能的碱基对列表，称为二级结构预测。早期的算法在很大程度上依靠寻找具有最小自由能的结构。但是，这些预测依赖于有效的简化自由能模型，该模型可能无法正确地将正确的结构识别为具有最低自由能的结构。有鉴于此，数据驱动的新方法不仅考虑了自由能，而且还研究了使用机器学习技术来学习主题的方法，最近在一些实验数据集上，该方法的性能优于基于自由能的算法。在这项工作中，我们介绍了新的ExpertRNA算法，该算法提供了一个模块化框架，可以轻松地合并任意数量的奖励（自由能或非参数/数据驱动）和二级结构预测算法。我们认为ExpertRNA的这种功能有可能平衡最新折叠工具的不同优势和劣势。我们在几个RNA序列结构数据集上测试ExpertRNA，并比较ExpertRNA与最新折叠算法的性能。我们发现，与使用的结构预测算法相比，ExpertRNA平均产生更准确的预测，