Monte Carlo Inverse RNA Folding.
| Autor: | Cazenave T; LAMSADE, Université Paris Dauphine - PSL, CNRS, Paris, France. cazenave@lamsade.dauphine.fr., Touzani H; LAMSADE, Université Paris Dauphine - PSL, CNRS, Paris, France. |
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| Jazyk: | angličtina |
| Zdroj: | Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2025; Vol. 2847, pp. 205-215. |
| DOI: | 10.1007/978-1-0716-4079-1_14 |
| Abstrakt: | The inverse RNA folding problem deals with designing a sequence of nucleotides that will fold into a desired target structure. Generalized Nested Rollout Policy Adaptation (GNRPA) is a Monte Carlo search algorithm for optimizing a sequence of choices. It learns a playout policy to intensify the search of the state space near the current best sequence. The algorithm uses a prior on the possible actions so as to perform non uniform playouts when learning the instance of problem at hand. We trained a transformer neural network on the inverse RNA folding problem using the Rfam database. This network is used to generate a prior for every Eterna100 puzzle. GNRPA is used with this prior to solve some of the instances of the Eterna100 dataset. The transformer prior gives better result than handcrafted heuristics. (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.) |
| Databáze: | MEDLINE |
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