參考文獻 |
1. Lei, J., et al., The antimicrobial peptides and their potential clinical applications. American Journal of Translational Research, 2019. 11(7): p. 3919-3931.
2. Magana, M., et al., The value of antimicrobial peptides in the age of resistance. Lancet Infectious Diseases, 2020. 20(9): p. E216-E230.
3. Murray, C.J.L., et al., Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis. Lancet, 2022. 399(10325): p. 629-655.
4. Zasloff, M., Antimicrobial peptides of multicellular organisms. Nature, 2002. 415(6870): p. 389-395.
5. Zasloff, M., Antimicrobial peptides of multicellular organisms: My perspective. Antimicrobial Peptides: Basics for Clinical Application, 2019. 1117: p. 3-6.
6. Wang, S., et al., Antimicrobial peptides as potential alternatives to antibiotics in food animal industry. International Journal of Molecular Sciences, 2016. 17(5).
7. Huan, Y.C., et al., Antimicrobial peptides: Classification, design, application and research progress in multiple fields. Frontiers in Microbiology, 2020. 11.
8. Nikaido, H., Molecular basis of bacterial outer membrane permeability revisited. Microbiology and Molecular Biology Reviews, 2003. 67(4): p. 593-+.
9. Hancock, R.E.W. and H.G. Sahl, Antimicrobial and host-defense peptides as new anti-infective therapeutic strategies. Nature Biotechnology, 2006. 24(12): p. 1551-1557.
10. Yan, W.H., et al., PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization. Plos Computational Biology, 2022. 18(9).
11. Niyonsaba, F., et al., Antimicrobial peptide derived from insulin-like growth factor-binding protein 5 activates mast cells via mas-related g protein-coupled receptor x2. Allergy, 2020. 75(1): p. 203-207.
12. Grønning, A.G.B., T. Kacprowski, and C. Schéele, MultiPep: A hierarchical deep learning approach for multi-label classification of peptide bioactivities. Biology Methods and Protocols, 2021. 6(1).
13. Pang, Y., et al., Integrating transformer and imbalanced multi-label learning to identify antimicrobial peptides and their functional activities. Bioinformatics, 2022. 38(24): p. 5368-5374.
14. Dee, W., LMPred: Predicting antimicrobial peptides using pre-trained language models and deep learning. Bioinformatics Advances, 2022. 2(1).
15. Elnaggar, A., et al., ProtTrans: Toward understanding the language of life through self-supervised learning. Ieee Transactions on Pattern Analysis and Machine Intelligence, 2022. 44(10): p. 7112-7127.
16. O′Shea, K. and R. Nash, An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458, 2015.
17. Hochreiter, S. and J. Schmidhuber, Long short-term memory. Neural Computation, 1997. 9(8): p. 1735-1780.
18. Ben-Baruch, E., et al., Asymmetric loss for multi-label classification. arXiv preprint arXiv:2009.14119, 2020.
19. Liu, B., K. Blekas, and G. Tsoumakas, Multi-label sampling based on local label imbalance. Pattern Recognition, 2022. 122.
20. Jhong, J.H., et al., dbAMP 2.0: Updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data. Nucleic Acids Research, 2022. 50(D1): p. D460-D470.
21. Shi, G.B., et al., DRAMP 3.0: An enhanced comprehensive data repository of antimicrobial peptides. Nucleic Acids Research, 2022. 50(D1): p. D488-D496.
22. Ye, G.Z., et al., LAMP2: A major update of the database linking antimicrobial peptides. Database-the Journal of Biological Databases and Curation, 2020.
23. Gawde, U., et al., CAMPR4: A database of natural and synthetic antimicrobial peptides. Nucleic Acids Res, 2023. 51(D1): p. D377-D383.
24. Pirtskhalava, M., et al., DBAASP v3: Database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics. Nucleic Acids Research, 2021. 49(D1): p. D288-D297.
25. Kim, H., et al., De novo generation of short antimicrobial peptides with enhanced stability and cell specificity. Journal of Antimicrobial Chemotherapy, 2014. 69(1): p. 121-132.
26. Fu, L.M., et al., CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics, 2012. 28(23): p. 3150-3152.
27. Tao, Y., D. Papadias, and X. Lian. Reverse knn search in arbitrary dimensionality. in Proceedings of the Thirtieth International Conference on Very Large Data Bases - Volume 30. 2004. Toronto, Canada: VLDB Endowment.
28. Chen, Z., et al., iFeature: A python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics, 2018. 34(14): p. 2499-2502.
29. Yang, Z., et al., Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems, 2019. 32.
30. Suzek, B.E., et al., UniRef clusters: A comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics, 2015. 31(6): p. 926-932.
31. Bateman, A., et al., Uniprot: A worldwide hub of protein knowledge. Nucleic Acids Research, 2019. 47(D1): p. D506-D515.
32. Raffel, C., et al., Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 2020. 21.
33. Spanig, S. and D. Heider, Encodings and models for antimicrobial peptide classification for multi-resistant pathogens. Biodata Mining, 2019. 12.
34. Chen, J.R., H.H. Cheong, and S.W.I. Siu, xDeep-AcPEP: Deep learning method for anticancer peptide activity prediction based on convolutional neural network and multitask learning. Journal of Chemical Information and Modeling, 2021. 61(8): p. 3789-3803.
35. Kawashima, S., et al., AAindex: Amino acid index database, progress report 2008. Nucleic Acids Research, 2008. 36: p. D202-D205.
36. Sandberg, M., et al., New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. Journal of Medicinal Chemistry, 1998. 41(14): p. 2481-2491.
37. Sechidis, K., G. Tsoumakas, and I. Vlahavas. On the stratification of multi-label data. in Machine Learning and Knowledge Discovery in Databases. 2011. Berlin, Heidelberg: Springer Berlin Heidelberg.
38. O′Shea, K. and R. Nash, An introduction to convolutional neural networks. 2015.
39. Vaswani, A., et al., Attention is all you need. Advances in neural information processing systems, 2017. 30.
40. Abadi, M., et al. Tensorflow: A system for large-scale machine learning. in Osdi. 2016. Savannah, GA, USA.
41. Li, Y., et al., MPMABP: A cnn and bi-lstm-based method for predicting multi-activities of bioactive peptides. Pharmaceuticals, 2022. 15(6).
42. Xiao, X., et al., iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Analytical Biochemistry, 2013. 436(2): p. 168-177.
43. Gull, S., N. Shamim, and F. Minhas, AMAP: Hierarchical multi-label prediction of biologically active and antimicrobial peptides. Computers in Biology and Medicine, 2019. 107: p. 172-181.
44. Zhou, J.-P., L. Chen, and Z.-H. Guo, Iatc-nrakel: An efficient multi-label classifier for recognizing anatomical therapeutic chemical classes of drugs. Bioinformatics, 2019. 36(5): p. 1391-1396.
45. McInnes, L., J. Healy, and J. Melville, Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426, 2018. |