面向RNA结构的最优文法研究
Towards Optimal Grammars for RNA Structures
AI总结：
针对RNA序列与结构联合压缩及结构预测需求，本研究开发自动系统性搜索随机文法的框架，经穷尽式搜索小型文法，找到性能超越人类专家设计文法的结果。
摘要：
在过往研究（Onokpasa、Wild、Wong，DCC 2023）中我们已证实：其一，针对RNA序列与结构的联合压缩，随机上下文无关文法是目前已知性能最优的压缩器；其二，压缩能力更强的文法，在从头结构预测任务中也表现更优。此前的文法均由人类专家手动构建。本研究开发了一套框架，用于自动、系统性地搜索具备更优RNA压缩（及预测）能力的随机文法搜索算法。我们对小型文法开展了穷尽式搜索，找到了性能超越人类专家设计文法的文法。
英文摘要：
In past work (Onokpasa, Wild, Wong, DCC 2023), we showed that (a) for joint compression of RNA sequence and structure, stochastic context-free grammars are the best known compressors and (b) that grammars which have better compression ability also show better performance in ab initio structure prediction. Previous grammars were manually curated by human experts. In this work, we develop a framework for automatic and systematic search algorithms for stochastic grammars with better compression (and prediction) ability for RNA. We perform an exhaustive search of small grammars and identify grammars that surpass the performance of human-expert grammars.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
