Identifying the secondary structure of an RNA is crucial for understanding its diverse regulatory functions. This paper focuses on how to enhance target identification in a Boltzmann ensemble of structures via chemical probing data. We employ an information-theoretic approach to solve the problem, via considering a variant of the Rényi-Ulam game. Our framework is centered around the ensemble tree, a hierarchical bi-partition of the input ensemble, that is constructed by recursively querying about whether or not a base pair of maximum information entropy is contained in the target. These queries are answered via relating local with global probing data, employing the modularity in RNA secondary structures. We present that leaves of the tree are comprised of sub-samples exhibiting a distinguished structure with high probability. In particular, for a Boltzmann ensemble incorporating probing data, which is well established in the literature, the probability of our framework correctly identifying the target in the leaf is greater than $$90\%$$ .

中文翻译：

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使用信息论增强RNA探测数据。

鉴定RNA的二级结构对于理解其多种调节功能至关重要。本文重点介绍如何通过化学探测数据来增强结构的玻尔兹曼合奏中的目标识别。通过考虑Rényi-Ulam游戏的一种变体，我们采用信息理论方法来解决该问题。我们的框架以集成树为中心，集成树是输入集成的分层双向部分，通过递归查询目标中是否包含最大信息熵的基对来构建。通过使用RNA二级结构中的模块化，通过将局部数据与全局探测数据相关联来回答这些查询。我们提出树的叶子是由子样本组成的，这些子样本具有很高的概率展现出杰出的结构。尤其是，