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RNA Secondary Structure Prediction Based on Energy Models.

In Methods in molecular biology (Clifton, N.J.)

This chapter introduces the RNA secondary structure prediction based on the nearest neighbor energy model, which is one of the most popular architectures of modeling RNA secondary structure without pseudoknots. We discuss the parameterization and the parameter determination by experimental and machine learning-based approaches as well as an integrated approach that compensates each other's shortcomings. Then, folding algorithms for the minimum free energy and the maximum expected accuracy using the dynamic programming technique are introduced. Finally, we compare the prediction accuracy of the method described so far with benchmark datasets.

Akiyama Manato, Sato Kengo

2023

Machine learning, Maximum expected accuracy, Minimum free energy, Nearest neighbor model, RNA secondary structure prediction, Thermodynamic parameters