Understanding RNA regulation mechanisms is crucial for developing targeted therapies for diseases and elucidating how cells control gene expression. This study presents a physics-informed machine learning framework to investigate dynamic regulatory mechanisms in RNA complexes, which when applied to the P-TEFb/Tat/TAR system involved in HIV-1 transcriptional activation and the aaRS/tRNA system essential for genetic translation, outperforms existing methods for uncovering latent regulatory networks.
- Haoquan Liu
- Yanan Zhu
- Yunjie Zhao