MOTIVATION:RNA loops have been modelled and clustered from solved 3D structures into ordered collections of recurrent non-canonical interactions called" RNA modules", available in databases. This work explores what information from such modules can be used to improve secondary structure prediction. We propose a bi-objective method for predicting RNA secondary structures by minimizing both an energy-based and a knowledge-based potential. The tool, called BiORSEO, outputs secondary structures corresponding to the optimal solutions from the Pareto set.RESULTS:We compare several approaches to predict secondary structures using inserted RNA modules information: two module data sources, Rna3Dmotif and The RNA 3D Motif Atlas, and different ways to score the module insertions: module size, module complexity, or module probability according to models like JAR3D and BayesPairing. We benchmark them against a large set of known secondary structures, including some state-of-the-art tools, and comment on the usefulness of the half physics-based, half data-based approach.AVAILABILITY:The software is available for download on the EvryRNA website, as well as the datasets.