IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v10y2011i1n6.html
   My bibliography  Save this article

Learning from Past Treatments and Their Outcome Improves Prediction of In Vivo Response to Anti-HIV Therapy

Author

Listed:
  • Saigo Hiroto
  • Altmann Andre
  • Bogojeska Jasmina
  • Müller Fabian
  • Nowozin Sebastian
  • Lengauer Thomas

Abstract

Infections with the human immunodeficiency virus type 1 (HIV-1) are treated with combinations of drugs. Unfortunately, HIV responds to the treatment by developing resistance mutations. Consequently, the genome of the viral target proteins is sequenced and inspected for resistance mutations as part of routine diagnostic procedures for ensuring an effective treatment. For predicting response to a combination therapy, currently available computer-based methods rely on the genotype of the virus and the composition of the regimen as input. However, no available tool takes full advantage of the knowledge about the order of and the response to previously prescribed regimens. The resulting high-dimensional feature space makes existing methods difficult to apply in a straightforward fashion. The machine learning system proposed in this work, sequence boosting, is tailored to exploiting such high-dimensional information, i.e. the extraction of longitudinal features, by utilizing the recent advancements in data mining and boosting.When applied to predicting the latest treatment outcome for 3,759 treatment-experienced patients from the EuResist integrated database, sequence boosting achieved superior performance compared to SVMs with RBF kernels. Moreover, sequence boosting allows an easy access to the discriminative treatment information.Analysis of feature importance values provided by our model confirmed known facts regarding HIV treatment. For instance, application of potent and recently licensed drugs was beneficial for patients, and, conversely, the patient group that was subject to NRTI mono-therapies in the past had poor treatment perspectives today. Furthermore, our model revealed novel biological insights. More precisely, the combination of previously used drugs with their in vivo response is more informative than the information of previously used drugs alone. Using this information improves the performance of systems for predicting therapy outcome.

Suggested Citation

  • 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.
  • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:6
    DOI: 10.2202/1544-6115.1604
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1544-6115.1604
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.2202/1544-6115.1604?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. 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.
    2. Foulkes A.S. & De Gruttola V., 2003. "Characterizing the Progression of Viral Mutations Over Time," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 859-867, January.
    Full references (including those not matched with items on IDEAS)

    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.

      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:bpj:sagmbi:v:10:y:2011:i:1:n:6. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

      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.