@inbook{5dfc2cc2d276431685ec12d391d1be06,
title = "RNA Secondary Structure Prediction Based on Energy Models",
abstract = "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{\textquoteright}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.",
keywords = "Machine learning, Maximum expected accuracy, Minimum free energy, Nearest neighbor model, RNA secondary structure prediction, Thermodynamic parameters",
author = "Manato Akiyama and Kengo Sato",
note = "Funding Information: This chapter was partially supported by Grant-in-Aid for JSPS Fellows (No. 18J21767) from the Japan Society for the Promotion of Science (JSPS) to M. A. and Grant-in-Aid for Scientific Research (B) (No. 19H04210) and Challenging Research (Exploratory) (No. 19K22897) from JSPS to K.S. Publisher Copyright: {\textcopyright} 2023, Springer Science+Business Media, LLC, part of Springer Nature.",
year = "2023",
doi = "10.1007/978-1-0716-2768-6_6",
language = "English",
series = "Methods in Molecular Biology",
publisher = "Humana Press Inc.",
pages = "89--105",
booktitle = "Methods in Molecular Biology",
}