Sparse Representation for Prediction of HIV-1 Protease Drug Resistance
HIV rapidly evolves drug resistance in response to antiviral drugs used in AIDS therapy. Estimating the specific resistance of a given strain of HIV to individual drugs from sequence data has important benefits for both the therapy of individual patients and the development of novel drugs. We have d...
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| Veröffentlicht in: | Proceedings of the ... SIAM International Conference on Data Mining Jg. 2013; S. 342 |
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| Abstract | HIV rapidly evolves drug resistance in response to antiviral drugs used in AIDS therapy. Estimating the specific resistance of a given strain of HIV to individual drugs from sequence data has important benefits for both the therapy of individual patients and the development of novel drugs. We have developed an accurate classification method based on the sparse representation theory, and demonstrate that this method is highly effective with HIV-1 protease. The protease structure is represented using our newly proposed encoding method based on Delaunay triangulation, and combined with the mutated amino acid sequences of known drug-resistant strains to train a machine-learning algorithm both for classification and regression of drug-resistant mutations. An overall cross-validated classification accuracy of 97% is obtained when trained on a publically available data base of approximately 1.5×10
known sequences (Stanford HIV database http://hivdb.stanford.edu/cgi-bin/GenoPhenoDS.cgi). Resistance to four FDA approved drugs is computed and comparisons with other algorithms demonstrate that our method shows significant improvements in classification accuracy. |
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| AbstractList | HIV rapidly evolves drug resistance in response to antiviral drugs used in AIDS therapy. Estimating the specific resistance of a given strain of HIV to individual drugs from sequence data has important benefits for both the therapy of individual patients and the development of novel drugs. We have developed an accurate classification method based on the sparse representation theory, and demonstrate that this method is highly effective with HIV-1 protease. The protease structure is represented using our newly proposed encoding method based on Delaunay triangulation, and combined with the mutated amino acid sequences of known drug-resistant strains to train a machine-learning algorithm both for classification and regression of drug-resistant mutations. An overall cross-validated classification accuracy of 97% is obtained when trained on a publically available data base of approximately 1.5×10
known sequences (Stanford HIV database http://hivdb.stanford.edu/cgi-bin/GenoPhenoDS.cgi). Resistance to four FDA approved drugs is computed and comparisons with other algorithms demonstrate that our method shows significant improvements in classification accuracy. HIV rapidly evolves drug resistance in response to antiviral drugs used in AIDS therapy. Estimating the specific resistance of a given strain of HIV to individual drugs from sequence data has important benefits for both the therapy of individual patients and the development of novel drugs. We have developed an accurate classification method based on the sparse representation theory, and demonstrate that this method is highly effective with HIV-1 protease. The protease structure is represented using our newly proposed encoding method based on Delaunay triangulation, and combined with the mutated amino acid sequences of known drug-resistant strains to train a machine-learning algorithm both for classification and regression of drug-resistant mutations. An overall cross-validated classification accuracy of 97% is obtained when trained on a publically available data base of approximately 1.5×104 known sequences (Stanford HIV database http://hivdb.stanford.edu/cgi-bin/GenoPhenoDS.cgi). Resistance to four FDA approved drugs is computed and comparisons with other algorithms demonstrate that our method shows significant improvements in classification accuracy.HIV rapidly evolves drug resistance in response to antiviral drugs used in AIDS therapy. Estimating the specific resistance of a given strain of HIV to individual drugs from sequence data has important benefits for both the therapy of individual patients and the development of novel drugs. We have developed an accurate classification method based on the sparse representation theory, and demonstrate that this method is highly effective with HIV-1 protease. The protease structure is represented using our newly proposed encoding method based on Delaunay triangulation, and combined with the mutated amino acid sequences of known drug-resistant strains to train a machine-learning algorithm both for classification and regression of drug-resistant mutations. An overall cross-validated classification accuracy of 97% is obtained when trained on a publically available data base of approximately 1.5×104 known sequences (Stanford HIV database http://hivdb.stanford.edu/cgi-bin/GenoPhenoDS.cgi). Resistance to four FDA approved drugs is computed and comparisons with other algorithms demonstrate that our method shows significant improvements in classification accuracy. |
| Author | Harrison, Robert W Yu, Xiaxia Weber, Irene T |
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| References_xml | – reference: 3052448 - Biochem Biophys Res Commun. 1988 Oct 14;156(1):297-303 – reference: 17065321 - Proc Natl Acad Sci U S A. 2006 Nov 14;103(46):17355-60 – reference: 10722492 - Antimicrob Agents Chemother. 2000 Apr;44(4):920-8 – reference: 11964529 - AIDS. 2002 Mar 29;16(5):727-36 – reference: 17243183 - Proteins. 2007 Apr 1;67(1):232-42 – reference: 22286877 - Intervirology. 2012;55(2):102-7 – reference: 10868275 - Biotechniques. 2000 Jun;28(6):1102, 1104 – reference: 12212924 - Antivir Ther. 2002 Jun;7(2):123-9 – reference: 19474477 - Antivir Ther. 2009;14(3):433-42 – reference: 22242794 - Biochemistry. 2012 Feb 7;51(5):1041-50 – reference: 2183354 - Science. 1990 Apr 20;248(4953):358-61 – reference: 21994585 - Viruses. 2009 Dec;1(3):1110-36 – reference: 1370910 - Biochemistry. 1992 Feb 4;31(4):954-8 – reference: 2479031 - Proc Natl Acad Sci U S A. 1989 Nov;86(22):8964-7 – reference: 12520007 - Nucleic Acids Res. 2003 Jan 1;31(1):298-303 – reference: 11013762 - Adv Pharmacol. 2000;49:111-46 – reference: 7816094 - Nature. 1995 Jan 12;373(6510):123-6 – reference: 18549313 - Clin Infect Dis. 2008 Jul 15;47(2):266-85 |
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| Title | Sparse Representation for Prediction of HIV-1 Protease Drug Resistance |
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