Biology-Guided Synergistic Learning of RNA Foundation Models for Secondary Structure Prediction

RNA secondary structure prediction aims to identify base-pairing patterns from primary sequences and requires rich biological priors for accurate modeling. Recent foundation models trained with large-scale pretraining encode rich biological priors into high-dimensional representations, yet their effective exploitation for downstream folding remains challenging. To address this issue, we revisit RNA secondary structure prediction from a representation learning perspective, focusing on a more effective exploitation of existing prior-aware representations rather than developing new deep learning modeling paradigms. We propose BioSynFold, a biology-guided synergistic learning framework for RNA secondary structure prediction that leverages foundation models from both model and data perspectives. From the model perspective, we propose a unified framework that integrates multiple RNA foundation models and introduces a progressive integration strategy to better leverage heterogeneous representations. From the data perspective, we exploit weak signals derived from sequence-level statistical and compositional properties to guide a mixture-of-experts routing mechanism, enabling condition-aware specialization and improving representation quality. Extensive experiments on multiple datasets indicate that BioSynFold significantly outperforms the state-of-the-art methods. Our code is available at https://anonymous.4open.science/r/BioSynFold.