Open Access
Issue
BIO Web Conf.
Volume 75, 2023
The 5th International Conference on Bioinformatics, Biotechnology, and Biomedical Engineering (BioMIC 2023)
Article Number 01008
Number of page(s) 6
Section Bioinformatics and Data Mining
DOI https://doi.org/10.1051/bioconf/20237501008
Published online 15 November 2023
  • P. D. Sun, C. E. Foster, and J. C. Boyington, “Overview of protein structural and functional folds.” Curr. Protoc. Protein Sci., vol. Chapter 17, pp. 1–189, 2004, doi: 10.1002/0471140864.ps1701s35. [Google Scholar]
  • Q. Jiang, X. Jin, S. J. Lee, and S. Yao, “Protein secondary structure prediction: A survey of the state of the art,” J. Mol. Graph. Model., vol. 76, pp. 379–402, 2017, doi: 10.1016/j.jmgm.2017.07.015. [CrossRef] [Google Scholar]
  • Y. F. Huang and S. Y. Chen, “Extracting physicochemical features to predict protein secondary structure,” Sci. World J., vol. 2013, 2013, doi: 10.1155/2013/347106. [Google Scholar]
  • K. Murata and M. Wolf, “Cryo-electron microscopy for structural analysis of dynamic biological,” BBA Gen. Subj., vol. 1862, no. 2, pp. 324–334, 2018, doi: 10.1016/j.bbagen.2017.07.020. [CrossRef] [Google Scholar]
  • E. Asgari, N. Poerner, A. C. McHardy, and M. R. K. Mofrad, “DeepPrime2Sec: Deep Learning for Protein Secondary Structure Prediction from the Primary Sequences,” bioRxiv, 2019, doi 10.1101/705426. [Google Scholar]
  • J. Zhou and O. G. Troyanskaya, “Deep supervised and convolutional generative stochastic network for protein secondary structure prediction,” in 31st International Conference on Machine Learning, ICML 2014, 2014, vol. 2, pp. 1121–1129. [Google Scholar]
  • S. I. Jalal, J. Zhong, and S. Kumar, “Protein Secondary Structure Prediction using Multi-input Convolutional Neural Network,” in Conference Proceedings IEEE SOUTHEASTCON, 2019, vol. 2019-April. doi: 10.1109/SoutheastCon42311.2019.9020333. [Google Scholar]
  • Z. Lin, J. Lanchantin, and Y. Qi, “MUST-CNN: A multilayer shift-And-stitch deep convolutional architecture for sequence-based protein structure prediction,” in 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016, pp. 27–34. [Google Scholar]
  • B. Zhang, J. Li, and Q. Lü, “Prediction of 8-state protein secondary structures by a novel deep learning architecture,” BMC Bioinformatics, vol. 19, no. 1, pp. 1–13, 2018, doi: 10.1186/s12859-018-2280-5. [CrossRef] [PubMed] [Google Scholar]
  • P. Majumder, M. Mitra, and B. B. Chaudhuri, “Ngram : a language-independent approach to IR and NLP,” in ICUKL, 2002, vol. 2. [Google Scholar]
  • S. Min, B. Lee, and S. Yoon, “Deep learning in bioinformatics,” Brief. Bioinform., vol. 18, no. 5, pp. 851–869, 2017, doi: 10.1093/bib/bbw068. [Google Scholar]
  • Y. Zhao, H. Zhang, and Y. Liu, “Protein secondary structure prediction based on generative confrontation and convolutional neural network,” IEEE Access, vol. 8, pp. 199171–199178, 2020, doi: 10.1109/ACCESS.2020.3035208. [CrossRef] [Google Scholar]
  • V. M. Sutanto, Z. I. Sukma, and A. Afiahayati, “Predicting Secondary Structure of Protein Using Hybrid of Convolutional Neural Network and Support Vector Machine,” Int. J. Intell. Eng. Syst., vol. 14, no. 1, pp. 232–243, 2020, doi: 10.22266/IJIES2021.0228.23. [Google Scholar]
  • W. Cavnar and J. Trenkle, “N-Gram-Based Text Categorization,” 2001. [Google Scholar]

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