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Research Article

Salp-J Colony Optimization-based advanced hybrid ensemble deep predictor with LSTM for protein structure prediction

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Received 18 Aug 2023, Accepted 04 Dec 2023, Published online: 06 Mar 2024

References

  • Bordoloi, H., & Sarma, K. K. (2012). Protein structure prediction using multiple artificial neural network classifier. Soft Computing Techniques in Vision Science, 395, 137–146.
  • Chen, C., Zhang, Q., Yu, B., Yu, Z., Lawrence, P. J., Ma, Q., & Zhang, Y. (2020). Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier. Computers in Biology and Medicine, 123, 103899. https://doi.org/10.1016/j.compbiomed.2020.103899
  • Cheng, J., Liu, Y., & Ma, Y. (2020). Protein secondary structure prediction based on the integration of CNN and LSTM model. Journal of Visual Communication and Image Representation, 71, 102844. https://doi.org/10.1016/j.jvcir.2020.102844
  • Chou, J.-S., & Truong, D.-N. (2021). A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation, 389, 125535. https://doi.org/10.1016/j.amc.2020.125535
  • CullPDB. (2023). Retrieved June, 2023, from https://github.com/topics/cullpdb.
  • Guo, Y., Wu, J., Ma, H., Wang, S., & Huang, J. (2022). Deep ensemble learning with atrous spatial pyramid networks for protein secondary structure prediction. Biomolecules, 12(6), 774. https://doi.org/10.3390/biom12060774
  • Hu, H., Li, Z., Elofsson, A., & Xie, S. (2019). A Bi-LSTM based ensemble algorithm for prediction of protein secondary structure. Applied Sciences, 9(17), 3538. https://doi.org/10.3390/app9173538
  • Jadhav, S., & Vyavahare, A. J. (2023). Novel protein structure prediction model using fused pipit adapted deep convolutional neural network classifier. In 2023 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 1–6). IEEE. https://doi.org/10.1109/ESCI56872.2023.10099768
  • Klausen, M. S., Jespersen, M. C., Nielsen, H., Jensen, K. K., Jurtz, V. I., Sønderby, C. K., Sommer, M. O. A., Winther, O., Nielsen, M., Petersen, B., & Marcatili, P. (2019). NetSurfp-2.0: Improved prediction of protein structural features by integrated deep learning. Proteins, 87(6), 520–527. https://doi.org/10.1002/prot.25674
  • Li, M. D., Zhao, H., Weng, X. W., & Han, T. (2016). A novel nature-inspired algorithm for optimization: Virus colony search. Advances in Engineering Software, 92, 65–88. https://doi.org/10.1016/j.advengsoft.2015.11.004
  • Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
  • Mirzaei, S., Sidi, T., Keasar, C., & Crivelli, S. (2019). Purely structural protein scoring functions using support vector machine and ensemble learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(5), 1515–1523, 1. https://doi.org/10.1109/TCBB.2016.2602269
  • Murakami, Y., & Mizuguchi, K. (2010). Applying the Naïve Bayes classifier with kernel density estimation to the prediction of protein–protein interaction sites. Bioinformatics, 26(15), 1841–1848. https://doi.org/10.1093/bioinformatics/btq302
  • ProteinNet dataset. (2023). Retrieved June, 2023, from https://github.com/aqlaboratory/proteinnet.
  • Sharma, A. K., & Srivastava, R. (2021). Protein secondary structure prediction using character bi-gram embedding and bi-LSTM. Current Bioinformatics, 16(2), 333–338. https://doi.org/10.2174/1574893615999200601122840
  • Söding, J. (2017). Big-data approaches to protein structure prediction. Science, 355(6322), 248–249. Jan. https://doi.org/10.1126/science.aal4512
  • Wei, L., Xing, P., Zeng, J., Chen, J., Su, R., & Guo, F. (2017). Improved prediction of protein–protein interactions using novel negative samples, features, and an ensemble classifier. Artificial Intelligence in Medicine, 83, 67–74. https://doi.org/10.1016/j.artmed.2017.03.001
  • Yu, S., Liao, B., Zhu, W., Peng, D., & Wu, F. (2023). Accurate prediction and key protein sequence feature identification of cyclins. Briefings in Functional Genomics, 22(5), elad014–419. https://doi.org/10.1093/bfgp/elad014
  • Yu, X., Negron, C., Huang, L., & Veldman, G. (2023). TransMHCII: A novel MHC-II binding prediction model built using a protein language model and an image classifier. Antibody Therapeutics, 6(2), 137–146. https://doi.org/10.1093/abt/tbad011
  • Zhang, B., Li, J., & Lü, Q. (2018). ′′ Prediction of 8-state protein secondary structures by a novel deep learning architecture. BMC Bioinformatics, 19(1), 293. https://doi.org/10.1186/s12859-018-2280-5
  • Zhang, G.-J., Ma, L.-F., Wang, X.-Q., & Zhou, X.-G. (2020). Secondary structure and contact guided differential evolution for protein structure prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(3), 1068–1081. https://doi.org/10.1109/TCBB.2018.2873691
  • Zhang, G.-J., Xie, T.-Y., Zhou, X.-G., Wang, L.-J., & Hu, J. (2021). Protein structure prediction using population-based algorithm guided by information entropy. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(2), 697–707. https://doi.org/10.1109/TCBB.2019.2921958
  • Zhao, Y., Zhang, H., & Liu, Y. (2020). Protein secondary structure prediction based on generative confrontation and convolutional neural network. IEEE Access. 8, 199171–199178. https://doi.org/10.1109/ACCESS.2020.3035208
  • Zhong, W., & Gu, F. (2022). Predicting local protein 3D structures using clustering deep recurrent neural network. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(1), 593–604. https://doi.org/10.1109/TCBB.2020.3005972
  • Zhou, S., Zou, H., Liu, C., Zang, M., & Liu, T. (2020). Combining deep neural networks for protein secondary structure prediction. IEEE Access. 8, 84362–84370. https://doi.org/10.1109/ACCESS.2020.2992084

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