DL-TCNN: Deep Learning-based Temporal Convolutional Neural Network for prediction of conformational B-cell epitopes
Author:
Publisher
Springer Science and Business Media LLC
Subject
Agricultural and Biological Sciences (miscellaneous),Environmental Science (miscellaneous),Biotechnology
Link
https://link.springer.com/content/pdf/10.1007/s13205-023-03716-7.pdf
Reference43 articles.
1. Angaitkar P, Janghel RR, Sahu TP (2023) gHPCSO: Gaussian distribution based hybrid particle cat swarm optimization for linear B-cell epitope prediction. Int J Inf Technol (singapore). https://doi.org/10.1007/S41870-023-01294-8/METRICS
2. Ansari HR, Raghava GP (2010) Identification of conformational B-cell epitopes in an antigen from its primary sequence. Immunome Res 6(1):6. https://doi.org/10.1186/1745-7580-6-6
3. Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
4. Buus S, Rockberg J, Forsström B, Nilsson P, Uhlen M, Schafer-Nielsen C (2012) High-resolution mapping of linear antibody epitopes using ultrahigh-density peptide microarrays. Mol Cell Proteomics 11(12):1790–1800. https://doi.org/10.1074/mcp.M112.020800
5. Chakravarty S, Varadarajan R (1999) Residue depth: a novel parameter for the analysis of protein structure and stability. Structure 7(7):723–732. https://doi.org/10.1016/S0969-2126(99)80097-5
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