Analysis on quantum reinforcement learning algorithms for prediction of protein sequence

Author:

Kalpana R.,Sathishkumar P. J.,Shenbagavalli B.,Subburaj S.

Publisher

Springer Science and Business Media LLC

Reference20 articles.

1. AlQuraishi, M.: Machine learning in protein structure prediction. Curr. Opin. Chem. Biol. 65, 1–8 (2021). https://doi.org/10.1016/j.cbpa.2021.04.005

2. Blei, D.M., Kucukelbir, A., McAuliffe, J.D.: Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112, 859–877 (2017)

3. Bohr, H., Bohr, J., Brunak, S., Cotterill, J., Fredholm, R.M., Lautrup, B., Petersen, S.: A novel approach to prediction of the 3-dimensional structures of protein backbones by neural networks, vol. 261, pp. 43–46. FEBS letters (1990)

4. Cho, K., Van Merri¨enboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder- decoder for statistical machine translation. arXiv preprint 2014, 1406.1078 (2014)

5. Das, P., Wadhawan, K., Chang, O., Sercu, T., Santos, C.D., Riemer, M., Chenthamarakshan, V., Padhi, I., Mojsilovic, A.: Pepcvae: semi-supervised targeted design of antimicrobial peptide sequences. arXiv preprint 2018, 1810.07743 (2018)

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