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Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning.

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This study benchmarks 21 RNA language models (LMs) for RNA analysis. Results show RNA-specific pretraining and evolutionary data are key for model performance, highlighting challenges in unified RNA representation.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning for Genomics

Background:

  • RNA language models (LMs) are emerging tools for RNA analysis, but their capabilities are not well understood.
  • Standardized evaluations are needed to characterize RNA LMs' representational power.

Purpose of the Study:

  • To provide a comprehensive zero-shot evaluation of 21 RNA LMs.
  • To assess RNA LMs' performance on structure prediction, classification, and fitness estimation.
  • To identify factors influencing RNA LM performance.

Main Methods:

  • Zero-shot evaluation of 21 RNA LMs and selected DNA LMs.
  • Assessed performance on RNA secondary structure prediction, RNA classification, and mutational fitness estimation.
  • Analyzed the impact of pretraining data and model architecture.

Main Results:

  • Significant performance variability observed across different RNA LMs.
  • RNA-specific, noncoding RNA-enriched pretraining is vital for structural information.
  • Evolutionary signals from multiple sequence alignments significantly improve performance.
  • Model scaling offers benefits, but architecture and objective choices are critical.

Conclusions:

  • Current RNA LMs exhibit trade-offs between structural, functional, and evolutionary representations.
  • Developing unified RNA representations remains a challenge.
  • This benchmark informs the development of next-generation RNA foundation models.