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Benchmarking Fine-Tuned RNA Language Models for Intronic Branch Point Prediction

P. Rodenas-Ruiz*, A. Saadat*, T. T. Tran*, O. Müller Smedt*, P. Zhang, J. Fellay

ICLR 2025 MLGenX Workshop (Tiny Papers track), 2025 · Equal contribution: P. Rodenas-Ruiz, A. Saadat, T. T. Tran, O. Müller Smedt.

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Abstract

Accurately predicting branch points in RNA splicing is fundamental to understanding splicing mechanisms and identifying pathogenic genetic variants. In this work, we fine-tuned and evaluated several RNA language models for branch point detection, achieving state-of-the-art performance. The top-performing model, ERNIE-RNA, surpassed the performance of previous leading models. Our findings suggest that hyperparameter optimization and extended training could significantly increase performance, establishing this work as a baseline for future research.

Cite (BibTeX)

@inproceedings{rodenasruiz2025benchmarking,
  title = {{Benchmarking Fine-Tuned RNA Language Models for Intronic Branch Point Prediction}},
  author = {Rodenas-Ruiz, P. and Saadat, A. and Tran, T. T. and Müller Smedt, O. and Zhang, P. and Fellay, J.},
  year = {2025},
  booktitle = {ICLR 2025 MLGenX Workshop (Tiny Papers track)},
  url = {https://openreview.net/forum?id=Q15Dg5lQou},
  note = {Equal contribution: P. Rodenas-Ruiz, A. Saadat, T. T. Tran, O. Müller Smedt.},
  abstract = {Accurately predicting branch points in RNA splicing is fundamental to understanding splicing mechanisms and identifying pathogenic genetic variants. In this work, we fine-tuned and evaluated several RNA language models for branch point detection, achieving state-of-the-art performance. The top-performing model, ERNIE-RNA, surpassed the performance of previous leading models. Our findings suggest that hyperparameter optimization and extended training could significantly increase performance, establishing this work as a baseline for future research.}
}

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