Learning from past treatments and their outcome improves prediction of in Vivo response to anti-HIV therapy

Hiroto Saigo, Andre Altmann, Jasmina Bogojeska, Fabian Mller, Sebastian Nowozin, Thomas Lengauer

研究成果: Contribution to journalArticle査読

8 被引用数 (Scopus)

抄録

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.

本文言語英語
論文番号6
ジャーナルStatistical Applications in Genetics and Molecular Biology
10
1
DOI
出版ステータス出版済み - 2011
外部発表はい

All Science Journal Classification (ASJC) codes

  • 統計学および確率
  • 分子生物学
  • 遺伝学
  • 計算数学

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