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DeepARG: A deep learning approach for predicting antibiotic resistance genes from metagenomic data

G. A. Arango-Argoty, E. Garner, A. Pruden, L. S. Heath, P. Vikesland, L. Zhang
doi: https://doi.org/10.1101/149328
G. A. Arango-Argoty
1Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
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E. Garner
2Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, USA
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A. Pruden
2Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, USA
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L. S. Heath
1Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
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P. Vikesland
2Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, USA
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L. Zhang
1Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
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  • For correspondence: lqzhang@vt.edu
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Posted June 12, 2017.
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DeepARG: A deep learning approach for predicting antibiotic resistance genes from metagenomic data
G. A. Arango-Argoty, E. Garner, A. Pruden, L. S. Heath, P. Vikesland, L. Zhang
bioRxiv 149328; doi: https://doi.org/10.1101/149328
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DeepARG: A deep learning approach for predicting antibiotic resistance genes from metagenomic data
G. A. Arango-Argoty, E. Garner, A. Pruden, L. S. Heath, P. Vikesland, L. Zhang
bioRxiv 149328; doi: https://doi.org/10.1101/149328

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