Computational understanding of non-coding RNA pairwise interactions

Non-coding RNAs (ncRNAs) govern a vast network of regulatory interactions within the cells, yet their pairwise relationships remain largely uncharted due to the complexity of RNA structure and the limits of current experimental methods. We present <i>CUPID</i> (Computational Understanding of Pairwise Interactions in ncRNA Data), a deep learning framework that predicts ncRNA-ncRNA interactions directly from primary sequence information. <i>CUPID</i> uses embeddings from a pre-trained RNA language model combined with a feed-forward classifier to identify patterns linked to molecular pairing. This approach avoids reliance on thermodynamic models or manual feature design and, unlike previously proposed models, is able to generalize across different types of ncRNAs, including long non-coding, circular, micro-, and small nuclear RNAs. By learning the hidden rules that govern RNA recognition, <i>CUPID</i> provides a scalable tool for exploring ncRNA interaction networks and advancing our understanding of RNA-based regulation.