Fetching the paper…
Reading the bibliography…
Generative Adversarial Networks (GANs) represent an attractive and novel approach to generate realistic data, such as genes, proteins, or drugs, in synthetic biology.
Ribosomally synthesized peptides with antimicrobial properties: biosynthesis, structure, function, and applications
Maria Papagianni · 2003
Earlier work this paper cites.
Antimicrobial peptides
Arash Izadpanah and Richard L. Gallo · 2004
Earlier work this paper cites.
Synthetic biology
Steven A. Benner and A. Michael Sismour · 2005
Earlier work this paper cites.
Scalable web services for the psipred protein analysis workbench
Daniel W. A. Buchan, Federico Minneci, Tim C. O. Nugent, Kevin Bryson, and David T. Jones · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
Apd3: the antimicrobial peptide database as a tool for research and education
Guangshun Wang, Xia Li, and Zhe Wang · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Real-valued (medical) time series generation with recurrent conditional gans
Cristóbal Esteban, Stephanie L. Hyland, and Gunnar Rätsch · 2017
Cited alongside, same era.
Cytogan: Generative modeling of cell images
Peter Goldsborough, Nick Pawlowski, Juan C Caicedo, Shantanu Singh, and Anne Carpenter · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martín Arjovsky, Vincent Dumoulin, and Aaron C. Courville · 2017
Cited alongside, same era.
Dilated convolutions for modeling long-distance genomic dependencies
Ankit Gupta and Alexander M Rush · 2017
Cited alongside, same era.
Molecular de novo design through deep reinforcement learning
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen · 2017
Later among the works it cites.
Gans for biological image synthesis
Anton Osokin, Anatole Chessel, Rafael Edgardo Carazo-Salas, and Federico Vaggi · 2017
Later among the works it cites.
Uniprot: the universal protein knowledgebase
Rolf Apweiler, Amos Bairoch, Cathy H. Wu, Winona C. Barker, Brigitte Boeckmann, Serenella Ferro, Elisabeth Gasteiger, Hongzhan Huang, Rodrigo Lopez, Michele Magrane, Maria J. Martin, Darren A. Natale, Claire O’Donovan, Nicole Redaschi, and Lai-Su L. Yeh · 2018
Closest in time.
Generative adversarial networks uncover epidermal regulators and predict single cell perturbations
Arsham Ghahramani, Fiona M Watt, and Nicholas M Luscombe · 2018
Closest in time.
Generative recurrent networks for de novo drug design
Anvita Gupta, Alex T. Müller, Berend J. H. Huisman, Jens A. Fuchs, Petra Schneider, and Gisbert Schneider · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Generating and designing DNA with deep generative models
N. Killoran, L. J. Lee, A. Delong, D. Duvenaud, and B. J. Frey · 2017
Cited alongside, same era.
modlamp: Python for antimicrobial peptides
Alex T. Müller, Gisela Gabernet, Jan A. Hiss, and Gisbert Schneider · 2017
Cited alongside, same era.
Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field
Dong Xu and Yang Zhang
Cited in the paper.
Closest in time.
Recurrent neural network model for constructive peptide design
Alex T. Müller, Jan A. Hiss, and Gisbert Schneider · 2018
Closest in time.
Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin H. S. Segler, Thierry Kogej, Christian Tyrchan, and Mark P. Waller · 2018
Closest in time.