Nucleic acid structure

Non-coding RNA

Biology

Computer science

Gene

Machine learning

Engineering

Folding (DSP implementation)

Genetics

Biochemistry

Algorithm

Regularization (linguistics)

Computational biology

Nucleic acid secondary structure

Deep learning

Overfitting

Protein secondary structure

Artificial intelligence

Artificial neural network

Robustness (evolution)

Parameterized complexity

Electrical engineering

Nature Communications

Accurate predictions of RNA secondary structures can help uncover the roles of functional non-coding RNAs. Although machine learning-based models have achieved high performance in terms of prediction accuracy, overfitting is a common risk for such highly parameterized models. Here we show that overfitting can be minimized when RNA folding scores learnt using a deep neural network are integrated together with Turner’s nearest-neighbor free energy parameters. Training the model with thermodynamic regularization ensures that folding scores and the calculated free energy are as close as possible. In computational experiments designed for newly discovered non-coding RNAs, our algorithm (MXfold2) achieves the most robust and accurate predictions of RNA secondary structures without sacrificing computational efficiency compared to several other algorithms. The results suggest that integrating thermodynamic information could help improve the robustness of deep learning-based predictions of RNA secondary structure.

RNA secondary structure prediction using deep learning with thermodynamic integration