Machine learning for RNA secondary structure prediction: a review of current methods and challenges

Schematic representation of thermodynamics-based RNA secondary structure prediction. The free energy of a structure is computed with the nearest neighbor model (left panel) as the sum of contributions from individual structural elements, enabling efficient dynamic programming algorithms to enumerate and predict the relative population of all of the possible secondary structures for a given RNA sequence (right panel). Secondary structure visualization generated with Forna (Kerpedjiev et al. 2015).