Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification
M. Riemenschneider, R. Senge, U. Neumann, E. Hüllermeier, D. Heider, BioData Mining 9 (2016).
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Journal Article
| English
Author
Riemenschneider, M.;
Senge, Robin;
Neumann, U.;
Hüllermeier, EykeLibreCat;
Heider, D.
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Publishing Year
Journal Title
BioData Mining
Volume
9
Issue
10
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Riemenschneider M, Senge R, Neumann U, Hüllermeier E, Heider D. Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. BioData Mining. 2016;9(10).
Riemenschneider, M., Senge, R., Neumann, U., Hüllermeier, E., & Heider, D. (2016). Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. BioData Mining, 9(10).
@article{Riemenschneider_Senge_Neumann_Hüllermeier_Heider_2016, title={Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification}, volume={9}, number={10}, journal={BioData Mining}, author={Riemenschneider, M. and Senge, Robin and Neumann, U. and Hüllermeier, Eyke and Heider, D.}, year={2016} }
Riemenschneider, M., Robin Senge, U. Neumann, Eyke Hüllermeier, and D. Heider. “Exploiting HIV-1 Protease and Reverse Transcriptase Cross-Resistance Information for Improved Drug Resistance Prediction by Means of Multi-Label Classification.” BioData Mining 9, no. 10 (2016).
M. Riemenschneider, R. Senge, U. Neumann, E. Hüllermeier, and D. Heider, “Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification,” BioData Mining, vol. 9, no. 10, 2016.
Riemenschneider, M., et al. “Exploiting HIV-1 Protease and Reverse Transcriptase Cross-Resistance Information for Improved Drug Resistance Prediction by Means of Multi-Label Classification.” BioData Mining, vol. 9, no. 10, 2016.
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