Wan, C;
Cozzetto, D;
Fa, R;
Jones, DT;
(2019)
Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks.
PLoS One
, 14
(7)
, Article e0209958. 10.1371/journal.pone.0209958.
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Abstract
Protein-protein interaction network data provides valuable information that infers direct links between genes and their biological roles. This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend to have similar functions. With the help of recently-developed network embedding feature generation methods and deep maxout neural networks, it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function. Our novel method, STRING2GO, successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information. The experimental results show that STRING2GO outperforms other protein-protein interaction network-based prediction methods and one benchmark method adopted in a recent large scale protein function prediction competition.
| Type: | Article |
|---|---|
| Title: | Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pone.0209958 |
| Publisher version: | https://doi.org/10.1371/journal.pone.0209958 |
| Language: | English |
| Additional information: | © 2019 Wan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Forecasting, Neural networks, Support vector machines, Algorithms, Gene ontologies, Protein interaction networks, Protein-protein interactions, Database and informatics methods |
| 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/10078735 |
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