Abstract
High-dimensional bioinformatics data, characterized by a large number of features relative to the number of samples, pose major challenges such as the ``curse of dimensionality,'' leading to overfitting, high computational cost, and poor generalization. Traditional feature selection methods often suffer from limited scalability and adaptability in such domains. We propose an LLM-in-the-loop reinforcement learning (RL) framework for bioinformatics feature selection, where the RL agent formulates feature selection as a sequential decision-making task, while the large language model (LLM) enhances the process in two ways: (1) guiding exploration through domain-informed advice, and (2) providing hybrid rewards that integrate data-driven performance with knowledge-driven evaluation. The LLM also produces explanations to improve interpretability for human experts without altering the RL policy update. Experiments on diverse bioinformatics datasets show that the LLM-in-the-loop framework outperforms baselines, achieves stable performance across downstream models, and converges faster than pure RL.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Wang, X., Agrawal, D., & Fu, Y. (2026). An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection. https://omanscience.com/en/articles/an-llm-in-the-loop-rl-framework-for-bioinformatics-feature-selection
MLA 9
Wang, Xinyuan, et al. "An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection." https://omanscience.com/en/articles/an-llm-in-the-loop-rl-framework-for-bioinformatics-feature-selection.
Chicago (author–date)
Wang, Xinyuan, Deepti Agrawal, and Yanjie Fu. 2026. "An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection." https://omanscience.com/en/articles/an-llm-in-the-loop-rl-framework-for-bioinformatics-feature-selection.
Harvard
Wang, X., Agrawal, D. and Fu, Y. (2026) 'An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection', Available at: https://omanscience.com/en/articles/an-llm-in-the-loop-rl-framework-for-bioinformatics-feature-selection.
Vancouver
Wang X, Agrawal D, Fu Y. An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection. https://omanscience.com/en/articles/an-llm-in-the-loop-rl-framework-for-bioinformatics-feature-selection
IEEE
X. Wang, D. Agrawal, and Y. Fu, "An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection," https://omanscience.com/en/articles/an-llm-in-the-loop-rl-framework-for-bioinformatics-feature-selection.