{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T22:34:08Z","timestamp":1781044448980,"version":"3.54.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T00:00:00Z","timestamp":1598832000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T00:00:00Z","timestamp":1598832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Mach Intell"],"DOI":"10.1038\/s42256-020-0222-1","type":"journal-article","created":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T12:04:06Z","timestamp":1598875446000},"page":"540-550","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks"],"prefix":"10.1038","volume":"2","author":[{"given":"Cen","family":"Wan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8626-3765","authenticated-orcid":false,"given":"David T.","family":"Jones","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,8,31]]},"reference":[{"key":"222_CR1","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-1-4939-3743-1_5","volume":"1446","author":"D Cozzetto","year":"2017","unstructured":"Cozzetto, D. & Jones, D. T. Computational methods for annotation transfers from sequence. Gene Ontol. Handb. 1446, 55\u201367 (2017).","journal-title":"Gene Ontol. Handb."},{"key":"222_CR2","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1038\/nmeth.2340","volume":"10","author":"P Radivojac","year":"2013","unstructured":"Radivojac, P. et al. A large-scale evaluation of computational protein function prediction. Nat. Methods 10, 221\u2013227 (2013).","journal-title":"Nat. Methods"},{"key":"222_CR3","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1186\/s13059-016-1037-6","volume":"17","author":"Y Jiang","year":"2016","unstructured":"Jiang, Y. et al. An expanded evaluation of protein function prediction methods shows an improvement in accuracy. Genome Biol. 17, 184 (2016).","journal-title":"Genome Biol."},{"key":"222_CR4","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1186\/s13059-019-1835-8","volume":"20","author":"N Zhou","year":"2019","unstructured":"Zhou, N. et al. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. Genome Biol. 20, 244 (2019).","journal-title":"Genome Biol."},{"key":"222_CR5","doi-asserted-by":"publisher","first-page":"e1005791","DOI":"10.1371\/journal.pcbi.1005791","volume":"13","author":"C Wan","year":"2017","unstructured":"Wan, C., Lees, J. G., Minneci, F., Orengo, C. A. & Jones, D. T. Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster. PLoS Comput. Biol. 13, e1005791 (2017).","journal-title":"PLoS Comput. Biol."},{"key":"222_CR6","doi-asserted-by":"publisher","first-page":"e0198216","DOI":"10.1371\/journal.pone.0198216","volume":"13","author":"R Fa","year":"2018","unstructured":"Fa, R., Cozzetto, D., Wan, C. & Jones, D. T. Predicting human protein function with multi-task deep neural networks. PLoS ONE 13, e0198216 (2018).","journal-title":"PLoS ONE"},{"key":"222_CR7","doi-asserted-by":"publisher","first-page":"e0209958","DOI":"10.1371\/journal.pone.0209958","volume":"14","author":"C Wan","year":"2019","unstructured":"Wan, C., Cozzetto, D., Fa, R. & Jones, D. T. Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks. PLoS ONE 14, e0209958 (2019).","journal-title":"PLoS ONE"},{"key":"222_CR8","unstructured":"Goodfellow, I. J. et al. Generative Adversarial Nets. In Advances in Neural Information Processing Systems (eds Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D. & Weinberger, K. Q.) Vol. 27, 2672\u20132680 (Curran Associates, 2014)."},{"key":"222_CR9","unstructured":"Radford, A., Metz, L. & Chintala, S. Unsupervised representation learning with deep convolutional generative adversarial networks. Preprint at https:\/\/arxiv.org\/abs\/1511.06434 (2015)."},{"key":"222_CR10","unstructured":"Arjovsky, M., Chintala, S. & Bottou, L. Wasserstein GAN. In Proceedings of the 34th International Conference on Machine Learning (PMLR, 2017)."},{"key":"222_CR11","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V. & Courville, A. Improved Training of Wasserstein GANs. In Advances in Neural Information Processing Systems (eds Guyon, I. et al.) Vol. 30, 5767\u20135777 (Curran Associates, 2017)."},{"key":"222_CR12","doi-asserted-by":"crossref","unstructured":"Mao, X. et al. Least squares generative adversarial networks. In 2017 IEEE International Conference on Computer Vision (ICCV) 2813\u20132821 (IEEE, 2017).","DOI":"10.1109\/ICCV.2017.304"},{"key":"222_CR13","unstructured":"Chen, X. et al. InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In Advances in Neural Information Processing Systems (eds Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, I. & Garnett, R.) Vol. 29, 2172\u20132180 (Curran Associates, 2016)."},{"key":"222_CR14","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Park, T., Isola, P. & Efros, A. A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In 2017 IEEE International Conference on Computer Vision (ICCV) 2223\u20132232 (IEEE, 2017).","DOI":"10.1109\/ICCV.2017.244"},{"key":"222_CR15","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T. & Efros, A. A. Image-to-image translation with conditional adversarial networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1125\u20131134 (IEEE, 2017).","DOI":"10.1109\/CVPR.2017.632"},{"key":"222_CR16","doi-asserted-by":"crossref","unstructured":"Choi, Y. et al. StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 8789\u20138797 (IEEE, 2018).","DOI":"10.1109\/CVPR.2018.00916"},{"key":"222_CR17","doi-asserted-by":"crossref","unstructured":"Souly, N., Spampinato, C. & Shah, M. Semi supervised semantic segmentation using generative adversarial network. In 2017 IEEE International Conference on Computer Vision (ICCV) 5688\u20135696 (IEEE, 2017).","DOI":"10.1109\/ICCV.2017.606"},{"key":"222_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yang, L. & Zheng, Y. Translating and segmenting multimodal medical volumes with cycle- and shape-consistency Generative Adversarial Network. In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 9242\u20139251 (IEEE, 2018).","DOI":"10.1109\/CVPR.2018.00963"},{"key":"222_CR19","doi-asserted-by":"crossref","unstructured":"Zhu, W., Xiang, X., Tran, T. D., Hager, G. D. & Xie, X. Adversarial deep structured nets for mass segmentation from mammograms. In 2018 IEEE 15th International Symposium on Biomedical Imaging 847\u2013850 (IEEE, 2018).","DOI":"10.1109\/ISBI.2018.8363704"},{"key":"222_CR20","doi-asserted-by":"crossref","unstructured":"Ledig, C. et al. Photo-realistic single image super-resolution using a generative adversarial network. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 4681\u20134690 (IEEE, 2017).","DOI":"10.1109\/CVPR.2017.19"},{"key":"222_CR21","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2017.2785879","volume":"37","author":"G Yang","year":"2017","unstructured":"Yang, G. et al. DAGAN: Deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE Trans. Med. Imaging 37, 1310\u20131321 (2017).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"222_CR22","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1016\/j.neuroimage.2018.07.043","volume":"181","author":"K Seeliger","year":"2018","unstructured":"Seeliger, K., G\u00fc\u00e7l\u00fc, U., Ambrogioni, L., G\u00fc\u00e7l\u00fct\u00fcrk, Y. & van Gerven, M. Generative adversarial networks for reconstructing natural images from brain activity. NeuroImage 181, 775\u2013785 (2018).","journal-title":"NeuroImage"},{"key":"222_CR23","doi-asserted-by":"publisher","first-page":"i603","DOI":"10.1093\/bioinformatics\/bty563","volume":"34","author":"X Wang","year":"2018","unstructured":"Wang, X., Dizaji, K. G. & Huang, H. Conditional generative adversarial network for gene expression inference. Bioinformatics 34, i603\u2013i611 (2018).","journal-title":"Bioinformatics"},{"key":"222_CR24","unstructured":"Dizaji, K. G., Wang, X. & Huang, H. Semi-supervised generative adversarial network for gene expression inference. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1435\u20131444 (ACM, 2018)."},{"key":"222_CR25","doi-asserted-by":"crossref","unstructured":"Ghahramani, A., Watt, F. M. & Luscombe, N. M. Generative adversarial networks simulate gene expression and predict perturbations in single cells. Preprint at BioRxiv https:\/\/www.biorxiv.org\/content\/10.1101\/262501v2 (2018).","DOI":"10.1101\/262501"},{"key":"222_CR26","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1038\/s42256-019-0017-4","volume":"1","author":"A Gupta","year":"2019","unstructured":"Gupta, A. & Zou, J. Feedback GAN for DNA optimizes protein functions. Nat. Mach. Intell. 1, 105\u2013111 (2019).","journal-title":"Nat. Mach. Intell."},{"key":"222_CR27","doi-asserted-by":"publisher","first-page":"6403","DOI":"10.1093\/nar\/gkaa325","volume":"48","author":"Y Wang","year":"2020","unstructured":"Wang, Y. et al. Synthetic promoter design in Escherichia coli based on a deep generative network. Nucl. Acids Res. 48, 6403\u20136412 (2020).","journal-title":"Nucl. Acids Res."},{"key":"222_CR28","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","volume":"321","author":"M Frid-Adar","year":"2018","unstructured":"Frid-Adar, M. et al. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321, 321\u2013331 (2018).","journal-title":"Neurocomputing"},{"key":"222_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, X., Liu, Y., Li, J., Wan, T. & Qin, Z. Emotion classification with data augmentation using generative adversarial networks. In Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018) (eds Phung, D. et al.) 349\u2013360 (Springer, 2018).","DOI":"10.1007\/978-3-319-93040-4_28"},{"key":"222_CR30","doi-asserted-by":"crossref","unstructured":"Volpi, R., Morerio, P., Savarese, S. & Murino, V. Adversarial feature augmentation for unsupervised domain adaptation. In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 5495\u20135504 (IEEE, 2018).","DOI":"10.1109\/CVPR.2018.00576"},{"key":"222_CR31","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1038\/s41467-019-14018-z","volume":"11","author":"M Marouf","year":"2020","unstructured":"Marouf, M. et al. Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nat. Commun. 11, 166 (2020).","journal-title":"Nat. Commun."},{"key":"222_CR32","doi-asserted-by":"publisher","first-page":"e63754","DOI":"10.1371\/journal.pone.0063754","volume":"8","author":"F Minneci","year":"2013","unstructured":"Minneci, F., Piovesan, D., Cozzetto, D. & Jones, D. T. FFPred 2.0: improved homology-independent prediction of gene ontology terms for eukaryotic protein sequences. PLoS ONE 8, e63754 (2013).","journal-title":"PLoS ONE"},{"key":"222_CR33","unstructured":"Lopez-Paz, D. & Oquab, M. Revisiting classifier two-sample tests. In Proceedings of the International Conference on Learning Representations (ICLR, 2017)."},{"key":"222_CR34","doi-asserted-by":"publisher","first-page":"E1732","DOI":"10.3390\/molecules22101732","volume":"22","author":"R Cao","year":"2017","unstructured":"Cao, R. et al. ProLanGO: protein function prediction using neural machine translation based on a recurrent neural network. Molecules 22, E1732 (2017).","journal-title":"Molecules"},{"key":"222_CR35","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, W. P. SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002).","journal-title":"J. Artif. Intell. Res."},{"key":"222_CR36","first-page":"1","volume":"18","author":"G Lema\u00eetre","year":"2017","unstructured":"Lema\u00eetre, G., Nogueira, F. & Aridas, C. K. Imbalanced-learn: a Python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18, 1\u20135 (2017).","journal-title":"J. Mach. Learn. Res."},{"key":"222_CR37","doi-asserted-by":"publisher","first-page":"W379","DOI":"10.1093\/nar\/gkz388","volume":"47","author":"R You","year":"2019","unstructured":"You, R. et al. NetGO: improving large-scale protein function prediction with massive network information. Nucleic Acids Res. 47, W379\u2013W387 (2019).","journal-title":"Nucleic Acids Res."},{"key":"222_CR38","doi-asserted-by":"publisher","first-page":"2465","DOI":"10.1093\/bioinformatics\/bty130","volume":"34","author":"R You","year":"2018","unstructured":"You, R. et al. GOLabeler: improving sequence-based large-scale protein function prediction by learning to rank. Bioinformatics 34, 2465\u20132473 (2018).","journal-title":"Bioinformatics"},{"key":"222_CR39","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011).","journal-title":"J. Mach. Learn. Res."}],"container-title":["Nature Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42256-020-0222-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-020-0222-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-020-0222-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T22:55:46Z","timestamp":1670367346000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42256-020-0222-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,31]]},"references-count":39,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["222"],"URL":"https:\/\/doi.org\/10.1038\/s42256-020-0222-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/730143","asserted-by":"object"}]},"ISSN":["2522-5839"],"issn-type":[{"value":"2522-5839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,31]]},"assertion":[{"value":"22 October 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}