Publisher
Springer Nature Switzerland
Reference11 articles.
1. Isaev, A., Deem, M.: Introduction to mathematical methods in bioinformatics. Phys. Today 58, 83 (2005). https://doi.org/10.1063/1.2138428
2. Jafari, R., Javidi, M.M., Kuchaki Rafsanjani, M.: Using deep reinforcement learning approach for solving the multiple sequence alignment problem. SN Applied Sciences 1(6), 1–12 (2019). https://doi.org/10.1007/s42452-019-0611-4
3. Lipman, D.J., Altschul, S.F., Kececioglu, J.D.: A tool for multiple sequence alignment. Proc. Natl. Acad. Sci. U.S.A. 86(12), 4412–5 (1989). https://doi.org/10.1073/pnas.86.12.4412
4. Mircea, I., Bocicor, M., Czibula, G.: Reinforcement learning based approach to multiple sequence alignment. Soft computing applications. Adv. Intell. Syst. Comput. 634, 54–70 (2018). https://doi.org/10.1007/978-3-319-62524-9_6
5. Mircea, I., Bocicor, M., Dincu, A.: On reinforcement learning based multiple sequence alignment. Studia Universitatis “Babes-Bolyai”, Informatica LIX, pp. 50–56 (2014)