标题： SAE-RNA：一种用于解释RNA语言模型表示的稀疏自编码器模型 显示英文标题

标题： SAE-RNA: A Sparse Autoencoder Model for Interpreting RNA Language Model Representations

摘要： 深度学习，特别是大型语言模型的进步，已经改变了生物分子建模，蛋白质方面的进展（例如ESM）激发了新兴的RNA语言模型，如RiNALMo。 然而，这些RNA语言模型内部如何以及编码了关于信使RNA（mRNA）或非编码RNA（ncRNA）家族的什么信息仍然不清楚。 我们提出了SAE-RNA，这是一种可解释性模型，用于分析RiNALMo表示并将其映射到已知的人类水平生物特征。 我们的工作将RNA可解释性框架视为预训练嵌入中的概念发现，无需端到端重新训练，并提供了实用工具来探究RNA LM可能关于ncRNA家族编码的内容。 该模型可以扩展到RNA组之间的紧密比较，并支持关于之前未被识别的关系的假设生成。 显示英文摘要

摘要： Deep learning, particularly with the advancement of Large Language Models, has transformed biomolecular modeling, with protein advances (e.g., ESM) inspiring emerging RNA language models such as RiNALMo. Yet how and what these RNA Language Models internally encode about messenger RNA (mRNA) or non-coding RNA (ncRNA) families remains unclear. We present SAE- RNA, interpretability model that analyzes RiNALMo representations and maps them to known human-level biological features. Our work frames RNA interpretability as concept discovery in pretrained embeddings, without end-to-end retraining, and provides practical tools to probe what RNA LMs may encode about ncRNA families. The model can be extended to close comparisons between RNA groups, and supporting hypothesis generation about previously unrecognized relationships.