Comprehensive benchmarking of large language models for RNA secondary structure prediction

• URL: http://arxiv.org/abs/2410.16212v1
• Date: Mon, 21 Oct 2024 17:12:06 GMT
• Title: Comprehensive benchmarking of large language models for RNA secondary structure prediction
• Authors: L. I. Zablocki, L. A. Bugnon, M. Gerard, L. Di Persia, G. Stegmayer, D. H. Milone,
• Abstract summary: RNA-LLM uses large datasets of RNA sequences to learn, in a self-supervised way, how to represent each RNA base with a semantically rich numerical vector. Among them, predicting the secondary structure is a fundamental task for uncovering RNA functional mechanisms. We present a comprehensive experimental analysis of several pre-trained RNA-LLM, comparing them for the RNA secondary structure prediction task in a unified deep learning framework.
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• Abstract: Inspired by the success of large language models (LLM) for DNA and proteins, several LLM for RNA have been developed recently. RNA-LLM uses large datasets of RNA sequences to learn, in a self-supervised way, how to represent each RNA base with a semantically rich numerical vector. This is done under the hypothesis that obtaining high-quality RNA representations can enhance data-costly downstream tasks. Among them, predicting the secondary structure is a fundamental task for uncovering RNA functional mechanisms. In this work we present a comprehensive experimental analysis of several pre-trained RNA-LLM, comparing them for the RNA secondary structure prediction task in an unified deep learning framework. The RNA-LLM were assessed with increasing generalization difficulty on benchmark datasets. Results showed that two LLM clearly outperform the other models, and revealed significant challenges for generalization in low-homology scenarios.

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