Abstract
With no HIV vaccine in sight, virologists need to know how the virus will react to a given combination drug therapy.
- Altmann, A. et al. Predicting the response to combination antiretroviral therapy: Retrospective validation of geno2pheno-THEO on a large clinical database. Journal of Infectious Diseases 199, 7 (Apr. 2009), 999--1006.Google ScholarCross Ref
- Altmann, A. et al. Improved prediction of response to antiretroviral combination therapy using the genetic barrier to drug resistance. Antiviral Therapy 12, 2 (2007), 169--178.Google Scholar
- Beerenwinkel, N. et al. Learning multiple evolutionary pathways from cross-sectional data. Journal of Computational Biology 12, 6 (July/Aug. 2005), 584--598.Google Scholar
- Beerenwinkel, N. et al. Estimating HIV evolutionary pathways and the genetic barrier to drug resistance. Journal of Infectious Diseases 191, 11 (June 2005), 1953--1960.Google ScholarCross Ref
- Beerenwinkel, N. et al. Geno2pheno: Estimating phenotypic drug resistance from HIV-1 genotypes. Nucleic Acids Research 31, 13 (July 2003), 3850--3855.Google ScholarCross Ref
- Beerenwinkel, N. et al. Diversity and complexity of HIV-1 drug resistance: A bioinformatics approach to predicting phenotype from genotype. Proceedings of the National Academy of Science USA 99, 12 (June 2002), 8271--8276.Google ScholarCross Ref
- Christianini, N. and Shawe-Taylor, J. An Introduction to Support Vector Machines. Cambridge University Press, Cambridge, U.K., 2000. Google ScholarDigital Library
- Desper, R. et al. Inferring tree models for oncogenesis from comparative genome hybridization data. Journal of Computational Biology 6, 1 (Spring 1999), 37--51.Google ScholarCross Ref
- Fields, B.N., Knipe, D.M., and Howley, P.M. Fields' Virology, Fifth Edition. Wolters Kluwer Health/ Lippincott Williams & Wilkins, Philadelphia, PA, 2007.Google Scholar
- Johnson, V.A. et al. Update of the drug-resistance mutations in HIV-1. Topics in HIV Medicine 16, 5 (Dec. 2008), 138--145.Google Scholar
- Landwehr, N., Hall, M., and Frank, E. Logistic model trees. Machine Learning 59, 1--2 (May 2005), 161--205. Google ScholarDigital Library
- Lengauer, T. et al. Bioinformatics prediction of HIV co-receptor usage. Nature Biotechnology 25, 12 (Dec. 2007), 1407--1410.Google ScholarCross Ref
- Lengauer, T. and Sing, T. Bioinformatics-assisted anti-HIV therapy. Nature Reviews Microbiology 4, 10 (Oct. 2006), 790--797.Google ScholarCross Ref
- Margulies, M. et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 7057 (Sept. 15, 2005), 376--380.Google ScholarCross Ref
- Rahnenführer, J. et al. Estimating cancer survival and clinical outcome based on genetic tumor progression scores. Bioinformatics 21, 10 (May 2005), 2438--2446. Google ScholarDigital Library
- Rhee, S.Y. et al. Human immunodeficiency virus reverse transcriptase and protease sequence database. Nucleic Acids Research 31, 1 (Jan. 2003), 298--303.Google ScholarCross Ref
- Roomp, K. et al. Arevir: A secure platform for designing personalized antiretroviral therapies against HIV. In Proceedings of the Third International Workshop on Data Integration in the Life Sciences (Hinxton, U.K. July 20--22). Springer Verlag, Berlin, Heidelberg, 2006, 185--194. Google ScholarDigital Library
- Rosen-Zvi, M. et al. Selecting anti-HIV therapies based on a variety of genomic and clinical factors. Bioinformatics 24, 13 (July 2008), 399--406. Google ScholarDigital Library
- Svicher, V. et al. Involvement of novel human immunodeficiency virus type 1 reverse transcriptase mutations in the regulation of resistance to nucleoside inhibitors. Journal of Virology 80, 14 (July 2006), 7186--7198.Google ScholarCross Ref
- UNAIDS. 2008 Report on the Global AIDS Epidemic. UNAIDS, Geneva, Switzerland, 2008; http://www.unaids.org/en/KnowledgeCentre/HIVData/GlobalReport/2008/2008_Global_report.aspGoogle Scholar
Index Terms
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