Deep learning methods are unable to predict RNA secondary structures
RNA structure prediction might seem like an ideal fit for machine learning, but it's more challenging than you might think. In this paper, we explore these difficulties by using synthetic data generated by ViennaRNA's RNAfold, offering a controlled environment to study how neural networks handle RNA secondary structure prediction. What we found suggests that the limitations seen in artificial settings can directly translate to real-world data, raising important questions about the effectiveness of current machine learning approaches in this field.