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Non-coding RNAs (ncRNAs) are RNA molecules that do not code for protein, but take part in biological processes, including gene expression. Interestingly, like proteins, they can fold into complex structures to perform their wide array of biological functions. Since the folded structure of a ncRNA may be critical to its function, many studies have attempted to exploit structural data to infer information, often using machine learning techniques. For instance, they have used predicted secondary structures as input features to various machine learning techniques, in order to classify RNA sequences. However, it is known that a strand of RNA can fold into more than one possible structure, and some strands even form different structures in vivo and in vitro. Furthermore, ncRNAs often function as RNA-protein complexes, which can affect structure. We therefore hypothesized that using a single predicted secondary structure for …