Leveraging deep learning algorithms for synthetic data generation to design and analyze biological networks
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s12038-022-00278-3.pdf
Reference64 articles.
1. Akrami H, Aydore S, Leahy RM and Joshi AA 2020 Robust variational autoencoder for tabular data with beta divergence. arXiv https://doi.org/10.48550/arXiv.2006.08204
2. Arjovsky M and Bottou L 2017 Towards principled methods for training generative adversarial networks. arXiv https://doi.org/10.48550/arXiv.1701.04862
3. Arjovsky M, Chintala S and Bottou L 2017 Wasserstein generative adversarial networks. Proc. 34th Int. Conf. Machine Learn. 214–223
4. Álvarez-Arenas A, Podolski-Renic A, Belmonte-Beitia J, Pesic M and Calvo GF 2019 Interplay of Darwinian selection, Lamarckian induction and microvesicle transfer on drug resistance in cancer. Sci. Rep. 9 9332
5. Barabási AL and Oltvai ZN 2004 Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5 101–113
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