About this Journal Submit a Manuscript Table of Contents
AIDS Research and Treatment
Volume 2012 (2012), Article ID 478467, 7 pages
http://dx.doi.org/10.1155/2012/478467
Research Article

Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data

1INSERM, UMR-S 943, 56 Boulevard Vincent Auriol, BP 335, 75625 Paris Cedex 13, France
2UPMC Univ Paris 06, UMR S943, Paris, France
3Service de Virologie, Hôpital Pitié-Salpêtrière, AP-HP, Paris, France
4Service des Maladies Infectieuses, Hôpital Saint Louis, AP-HP, Paris, France

Received 13 May 2011; Revised 8 November 2011; Accepted 14 January 2012

Academic Editor: Christina Ramirez Kitchen

Copyright © 2012 Allal Houssaïni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. Lorenzi, M. Opravil, B. Hirschel et al., “Impact of drug resistance mutations on virologic response to salvage therapy,” AIDS, vol. 13, no. 2, pp. F17–F21, 1999. View at Publisher · View at Google Scholar
  2. S. Palmer, R. W. Shafer, and T. C. Merigan, “Highly drug-resistant HIV-1 clinical isolates are cross-resistant to many antiretroviral compounds in current clinical development,” AIDS, vol. 13, no. 6, pp. 661–667, 1999. View at Scopus
  3. J. Aslanzadeh, “HIV resistance testing: an update,” Annals of Clinical and Laboratory Science, vol. 32, no. 4, pp. 406–413, 2002. View at Scopus
  4. D. Costagliola, D. Descamps, L. Assoumou et al., “Prevalence of HIV-1 drug resistance in treated patients: a French nationwide study,” Journal of Acquired Immune Deficiency Syndromes, vol. 46, no. 1, pp. 12–18, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. R. W. Shafer, R. Kantor, and M. J. Gonzales, “The genetic basis of HIV-1 resistance to reverse transcriptase and protease inhibitors,” AIDS Reviews, vol. 2, no. 4, pp. 211–228, 2000. View at Scopus
  6. K. Wang, E. Jenwitheesuk, R. Samudrala, and J. E. Mittler, “Simple linear model provides highly accurate genotypic predictions of HIV-1 drug resistance,” Antiviral Therapy, vol. 9, no. 3, pp. 343–352, 2004. View at Scopus
  7. R. Kantor, R. Machekano, M. J. Gonzales, K. Dupnik, J. M. Schapiro, and R. W. Shafer, “Human immunodeficiency virus reverse transcriptase and protease sequence database: an expanded data model integrating natural language text and sequence analysis programs,” Nucleic Acids Research, vol. 29, no. 1, pp. 296–299, 2001. View at Scopus
  8. N. Beerenwinkel, B. Schmidt, H. Walter, et al., “Quantitative phenotype prediction by support vector machines,” Antiviral Therapy, vol. 7, pp. S97–S97, 2002.
  9. D. Wang, V. DeGruttola, S. Hammer, et al., “A collaborative HIV resistance response database initiative: predicting virological response using neural network models,” Antiviral Therapy, vol. 7, pp. S128–S128, 2002.
  10. A. D. Sevin, V. DeGruttola, M. Nijhuis et al., “Methods for investigation of the relationship between drug-susceptibility phenotype and human immunodeficiency virus type 1 genotype with applications to AIDS Clinical Trials Group 333,” Journal of Infectious Diseases, vol. 182, no. 1, pp. 59–67, 2000. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Drǎghici and R. B. Potter, “Predicting HIV drug resistance with neural networks,” Bioinformatics, vol. 19, no. 1, pp. 98–107, 2003. View at Publisher · View at Google Scholar · View at Scopus
  12. N. Beerenwinkel, P. Knupfer, and A. Tresch, “Learning monotonic genotype-phenotype maps,” Statistical Applications in Genetics and Molecular Biology, vol. 10, no. 1, article 3, 2011. View at Publisher · View at Google Scholar
  13. L. Assoumou, F. Brun-Vézinet, A. Cozzi-Lepri et al., “Initiatives for developing and comparing genotype interpretation systems: external validation of existing systems for didanosine against virological response,” Journal of Infectious Diseases, vol. 198, no. 4, pp. 470–480, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Ravela, B. J. Betts, F. Brun-Vézinet et al., “HIV-1 protease and reverse transcriptase mutation patterns responsible for discordances between genotypic drug resistance interpretation algorithms,” Journal of Acquired Immune Deficiency Syndromes, vol. 33, no. 1, pp. 8–14, 2003. View at Scopus
  15. A. J. Kandathil, R. Kannangai, O. C. Abraham, S. A. Pulimood, M. A. Jensen, and G. Sridharan, “A comparison of interpretation by three different HIV type 1 genotypic drug resistance algorithms using sequences from non-clade B HIV type 1 strains,” AIDS Research and Human Retroviruses, vol. 25, no. 3, pp. 315–318, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. L. Assoumou, A. Houssaïni, D. Costagliola, and P. Flandre, “Relative contributions of baseline patient characteristics and the choice of statistical methods to the variability of genotypic resistance scores: the example of didanosine,” Journal of Antimicrobial Chemotherapy, vol. 65, no. 4, pp. 752–760, 2010. View at Publisher · View at Google Scholar
  17. H. Saigo, A. Altmann, J. Bogojeska, F. Mller, S. Nowozin, and T. Lengauer, “Learning from past treatments and their outcome improves prediction of in vivo response to anti-HIV therapy,” Statistical Applications in Genetics and Molecular Biology, vol. 10, no. 1, article 6, 2011. View at Publisher · View at Google Scholar
  18. M. J. van der Laan and S. Dudoit, “Unified cross-validation methodology for selection among estimators and a general cross-validated adaptive epsilon-net estimator: finite sample oracle inequalities and examples,” Tech. Rep. number 130, Division of Biostatistics, University of California, Berkeley, Calif, USA, http://www.bepress.com/ucbbiostat/paper130/, 2003.
  19. M. J. van der Laan and R. Sherri, Targeted Learning: Causal Inference for Observational and Experimental Data, Springer, 2011.
  20. S. E. Sinisi, E. C. Polley, M. L. Petersen, S. Y. Rhee, and M. J. van der Laan, “Super learning: an application to the prediction of HIV-1 drug resistance,” Statistical Applications in Genetics and Molecular Biology, vol. 6, no. 1, article 7, pp. 1–24, 2007. View at Scopus
  21. M. J. van der Laan, E. C. Polley, and A. E. Hubbard, Super Learner, Working Paper Series, U.C. Berkeley Division of Biostatistics, 2007.
  22. E. C. Polley and M. J. van der Laan, Super Learner in Prediction, Working Paper 266, U.C. Berkeley Division of Biostatistics, 2010.
  23. L. Rosasco, E. de Vito, A. Caponnetto, M. Piana, and A. Verri, “Are loss functions all the same?” Neural Computation, vol. 16, no. 5, pp. 1063–1076, 2004. View at Publisher · View at Google Scholar · View at Scopus
  24. J. M. Molina, A. G. Marcelin, J. Pavie et al., “Didanosine in HIV-1-infected patients experiencing failure of antiretroviral therapy: a randomized placebo-controlled trial,” Journal of Infectious Diseases, vol. 191, no. 6, pp. 840–847, 2005. View at Publisher · View at Google Scholar · View at Scopus
  25. I. C. Marschner, R. A. Betensky, V. DeGruttola, S. M. Hammer, and D. R. Kuritzkes, “Clinical trials using HIV-1 RNA-based primary endpoints: statistical analysis and potential biases,” Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology, vol. 20, no. 3, pp. 220–227, 1999.
  26. P. Flandre, C. Durier, D. Descamps, O. Launay, and V. Joly, “On the use of magnitude of reduction in HIV-1 RNA in clinical trials: statistical analysis and potential biases,” Journal of Acquired Immune Deficiency Syndromes, vol. 30, no. 1, pp. 59–64, 2002. View at Publisher · View at Google Scholar · View at Scopus
  27. P. Flandre, A. Alcais, D. Descamps, L. Morand-Joubert, and V. Joly, “Estimating and comparing reduction in HIV-1 RNA in clinical trials using methods for interval censored data,” Journal of Acquired Immune Deficiency Syndromes, vol. 35, no. 3, pp. 286–292, 2004. View at Publisher · View at Google Scholar · View at Scopus
  28. S. Y. Rhee, J. Taylor, G. Wadhera, A. Ben-Hur, D. L. Brutlag, and R. W. Shafer, “Genotypic predictors of human immunodeficiency virus type 1 drug resistance,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 46, pp. 17355–17360, 2006. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Rabinowitz, L. Myers, M. Banjevic et al., “Accurate prediction of HIV-1 drug response from the reverse transcriptase and protease amino acid sequences using sparse models created by convex optimization,” Bioinformatics, vol. 22, no. 5, pp. 541–549, 2006. View at Publisher · View at Google Scholar · View at Scopus
  30. I. Ruczinski, C. Kooperberg, and M. Leblanc, “Logic regression,” Journal of Computational and Graphical Statistics, vol. 12, no. 3, pp. 475–511, 2003. View at Publisher · View at Google Scholar · View at Scopus
  31. S. E. Sinisi and M. J. van der Laan, “Deletion/substitution/addition algorithm in learning with applications in genomics,” Statistical Applications in Genetics and Molecular Biology, vol. 3, no. 1, article 18, 2004. View at Scopus
  32. L. Breiman, J. Friedman, C. Stone, and R. A. Olshen, Classification and Regression Trees, Chapman and Hall/CRC, 1984.
  33. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Scopus
  34. M. C. F. Prosperi, A. Altmann, M. Rosen-Zvi et al., “Investigation of expert rule bases, logistic regression, and non-linear machine learning techniques for predicting response to antiretroviral treatment,” Antiviral Therapy, vol. 14, no. 3, pp. 433–442, 2009. View at Scopus
  35. A. G Marcelin, P. Flandre, J. Pavie, et al., “New genotypic score comprising mutations impacting negatively and positively the virological response to didanosine in treatment-experienced patients from the randomized didanosine add on Jaguar study,” Antiviral Therapy, vol. 9, pp. U102–U102, 2004.
  36. B. Masquelier, K. L. Assoumou, D. Descamps et al., “Clinically validated mutation scores for HIV-1 resistance to fosamprenavir/ritonavir,” Journal of Antimicrobial Chemotherapy, vol. 61, no. 6, pp. 1362–1368, 2008. View at Publisher · View at Google Scholar · View at Scopus
  37. S. Vora, A. G. Marcelin, H. F. Günthard et al., “Clinical validation of atazanavir/ritonavir genotypic resistance score in protease inhibitor-experienced patients,” AIDS, vol. 20, no. 1, pp. 35–40, 2006. View at Scopus
  38. P. Flandre, A. G. Marcelin, J. Pavie et al., “Comparison of tests and procedures to build clinically relevant genotypic scores: application to the Jaguar study,” Antiviral Therapy, vol. 10, no. 4, pp. 479–487, 2005. View at Scopus
  39. A. G. DiRienzo, V. DeGruttola, B. Larder, and K. Hertogs, “Non-parametric methods to predict HIV drug susceptibility phenotype from genotype,” Statistics in Medicine, vol. 22, no. 17, pp. 2785–2798, 2003. View at Publisher · View at Google Scholar · View at Scopus