Department of Bioinformatics, University of Duisburg-Essen, Essen, Germany.
Bioinformatics. 2013 Aug 15;29(16):1946-52. doi: 10.1093/bioinformatics/btt331. Epub 2013 Jun 21.
Antiretroviral treatment regimens can sufficiently suppress viral replication in human immunodeficiency virus (HIV)-infected patients and prevent the progression of the disease. However, one of the factors contributing to the progression of the disease despite ongoing antiretroviral treatment is the emergence of drug resistance. The high mutation rate of HIV can lead to a fast adaptation of the virus under drug pressure, thus to failure of antiretroviral treatment due to the evolution of drug-resistant variants. Moreover, cross-resistance phenomena have been frequently found in HIV-1, leading to resistance not only against a drug from the current treatment, but also to other not yet applied drugs. Automatic classification and prediction of drug resistance is increasingly important in HIV research as well as in clinical settings, and to this end, machine learning techniques have been widely applied. Nevertheless, cross-resistance information was not taken explicitly into account, yet.
In our study, we demonstrated the use of cross-resistance information to predict drug resistance in HIV-1. We tested a set of more than 600 reverse transcriptase sequences and corresponding resistance information for six nucleoside analogues. Based on multilabel classification models and cross-resistance information, we were able to significantly improve overall prediction accuracy for all drugs, compared with single binary classifiers without any additional information. Moreover, we identified drug-specific patterns within the reverse transcriptase sequences that can be used to determine an optimal order of the classifiers within the classifier chains. These patterns are in good agreement with known resistance mutations and support the use of cross-resistance information in such prediction models.
Supplementary data are available at Bioinformatics online.
抗逆转录病毒治疗方案可以充分抑制人类免疫缺陷病毒 (HIV) 感染患者体内的病毒复制，从而阻止疾病的进展。然而，尽管正在进行抗逆转录病毒治疗，但导致疾病进展的因素之一是出现耐药性。HIV 的高突变率可导致病毒在药物压力下快速适应，从而由于耐药变异体的出现而导致抗逆转录病毒治疗失败。此外，HIV-1 中经常发现交叉耐药现象，导致不仅对当前治疗中的药物产生耐药性，而且对其他尚未应用的药物也产生耐药性。自动分类和预测耐药性在 HIV 研究以及临床环境中变得越来越重要，为此，机器学习技术得到了广泛应用。然而，交叉耐药信息尚未被明确考虑在内。
在我们的研究中，我们展示了使用交叉耐药信息来预测 HIV-1 中的耐药性。我们测试了一组超过 600 个逆转录酶序列和对应六种核苷类似物的耐药信息。基于多标签分类模型和交叉耐药信息，与没有任何附加信息的单个二进制分类器相比，我们能够显著提高所有药物的总体预测准确性。此外，我们在逆转录酶序列中确定了特定于药物的模式，这些模式可用于确定分类器链中分类器的最佳顺序。这些模式与已知的耐药突变一致，并支持在这些预测模型中使用交叉耐药信息。
补充数据可在 Bioinformatics 在线获取。
