RNA Secondary Structure Prediction Based on Energy Models.
| Autor: | Akiyama M; Department of Biosciences and Informatics, Keio University, Yokohama, Japan., Sato K; School of System Design and Technology, Tokyo Denki University, Tokyo, Japan. satoken@mail.dendai.ac.jp. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2023; Vol. 2586, pp. 89-105. |
| DOI: | 10.1007/978-1-0716-2768-6_6 |
| Abstrakt: | 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. (© 2023. Springer Science+Business Media, LLC, part of Springer Nature.) |
| Databáze: | MEDLINE |
| Externí odkaz: |
|