Jones, D;
Wan, C;
(2020)
Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks.
Nature Machine Intelligence
, 2
pp. 540-550.
10.1038/s42256-020-0222-1.
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Abstract
Protein function prediction is a challenging but important task in bioinformatics. Many prediction methods have been developed, but are still limited by the bottleneck on training sample quantity. Therefore, it is valuable to develop a data augmentation method that can generate high-quality synthetic samples to further improve the accuracy of prediction methods. In this work, we propose a novel generative adversarial networks-based method, FFPred-GAN, to accurately learn the high-dimensional distributions of protein sequence-based biophysical features and also generate high-quality synthetic protein feature samples. The experimental results suggest that the synthetic protein feature samples are successful in improving the prediction accuracy for all three domains of Gene Ontology through augmentation of the original training protein feature samples.
| Type: | Article |
|---|---|
| Title: | Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s42256-020-0222-1 |
| Publisher version: | https://doi.org/10.1038/s42256-020-0222-1 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10106274 |
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