IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v58y2002i1p145-156.html
   My bibliography  Save this article

Characterizing the Relationship Between HIV-1 Genotype and Phenotype: Prediction-Based Classification

Author

Listed:
  • A. S. Foulkes
  • V. De Gruttola

Abstract

No abstract is available for this item.

Suggested Citation

  • A. S. Foulkes & V. De Gruttola, 2002. "Characterizing the Relationship Between HIV-1 Genotype and Phenotype: Prediction-Based Classification," Biometrics, The International Biometric Society, vol. 58(1), pages 145-156, March.
    • Handle: RePEc:bla:biomet:v:58:y:2002:i:1:p:145-156
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2002.00145.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mark R. Segal & Michael P. Cummings & Alan E. Hubbard, 2001. "Relating Amino Acid Sequence to Phenotype: Analysis of Peptide-Binding Data," Biometrics, The International Biometric Society, vol. 57(2), pages 632-643, June.
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    3. Daowen Zhang & Marie Davidian, 2001. "Linear Mixed Models with Flexible Distributions of Random Effects for Longitudinal Data," Biometrics, The International Biometric Society, vol. 57(3), pages 795-802, September.
    4. Ciampi, Antonio & Thiffault, Johanne & Nakache, Jean-Pierre & Asselain, Bernard, 1986. "Stratification by stepwise regression, correspondence analysis and recursive partition: a comparison of three methods of analysis for survival data with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 4(3), pages 185-204, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Saigo Hiroto & Altmann Andre & Bogojeska Jasmina & Müller Fabian & Nowozin Sebastian & Lengauer Thomas, 2011. "Learning from Past Treatments and Their Outcome Improves Prediction of In Vivo Response to Anti-HIV Therapy," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-32, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sipan Aslan & Ceylan Yozgatligil & Cem Iyigun, 2018. "Temporal clustering of time series via threshold autoregressive models: application to commodity prices," Annals of Operations Research, Springer, vol. 260(1), pages 51-77, January.
    2. Thiemo Fetzer & Samuel Marden, 2017. "Take What You Can: Property Rights, Contestability and Conflict," Economic Journal, Royal Economic Society, vol. 0(601), pages 757-783, May.
    3. Francesco Trebbi & Eric Weese, 2019. "Insurgency and Small Wars: Estimation of Unobserved Coalition Structures," Econometrica, Econometric Society, vol. 87(2), pages 463-496, March.
    4. Daniel Agness & Travis Baseler & Sylvain Chassang & Pascaline Dupas & Erik Snowberg, 2022. "Valuing the Time of the Self-Employed," Working Papers 2022-2, Princeton University. Economics Department..
    5. Khanh Duong, 2024. "Is meritocracy just? New evidence from Boolean analysis and Machine learning," Journal of Computational Social Science, Springer, vol. 7(2), pages 1795-1821, October.
    6. Batool, Fatima & Hennig, Christian, 2021. "Clustering with the Average Silhouette Width," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    7. Alexandra-Nicoleta Ciucu-Durnoi & Camelia Delcea, 2024. "Ecosystem Degradation in Romania: Exploring the Core Drivers," Stats, MDPI, vol. 7(1), pages 1-16, January.
    8. Nicoleta Serban & Huijing Jiang, 2012. "Multilevel Functional Clustering Analysis," Biometrics, The International Biometric Society, vol. 68(3), pages 805-814, September.
    9. Audrey Mauguen & Emily C. Zabor & Nancy E. Thomas & Marianne Berwick & Venkatraman E. Seshan & Colin B. Begg, 2017. "Defining Cancer Subtypes With Distinctive Etiologic Profiles: An Application to the Epidemiology of Melanoma," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 54-63, January.
    10. Anis Hoayek & Didier Rullière, 2024. "Assessing clustering methods using Shannon's entropy," Post-Print hal-03812055, HAL.
    11. Orietta Nicolis & Jean Paul Maidana & Fabian Contreras & Danilo Leal, 2024. "Analyzing the Impact of COVID-19 on Economic Sustainability: A Clustering Approach," Sustainability, MDPI, vol. 16(4), pages 1-30, February.
    12. Zhang, Daowen & Davidian, Marie, 2004. "Likelihood and conditional likelihood inference for generalized additive mixed models for clustered data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 90-106, October.
    13. Huang, Pei & McCarl, Bruce A., 2014. "Estimating Decadal Climate Variability Effects on Crop Yields: A Bayesian Hierarchical Approach," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169828, Agricultural and Applied Economics Association.
    14. Osbert C Zalay, 2020. "Blind method for discovering number of clusters in multidimensional datasets by regression on linkage hierarchies generated from random data," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-28, January.
    15. Roberto Benedetti & Monica Pratesi & Nicola Salvati, 2013. "Local stationarity in small area estimation models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(1), pages 81-95, March.
    16. Matthew P. Mulè & Andrew J. Martins & John S. Tsang, 2022. "Normalizing and denoising protein expression data from droplet-based single cell profiling," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    17. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    18. Yaeji Lim & Hee-Seok Oh & Ying Kuen Cheung, 2019. "Multiscale Clustering for Functional Data," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 368-391, July.
    19. Forzani, Liliana & Gieco, Antonella & Tolmasky, Carlos, 2017. "Likelihood ratio test for partial sphericity in high and ultra-high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 18-38.
    20. Praene, Jean Philippe & Payet, Mahéva & Bénard-Sora, Fiona, 2018. "Sustainable transition in small island developing states: Assessing the current situation," Utilities Policy, Elsevier, vol. 54(C), pages 86-91.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:58:y:2002:i:1:p:145-156. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.