Simple Linear Model Provides Highly Accurate Genotypic Predictions of HIV-1 Drug Resistance
Simple Linear Model Provides Highly Accurate Genotypic Predictions of HIV-1 Drug Resistance
复制标题
简单线性模型提供高度准确的 HIV-1 耐药性基因型预测
作者：
Kai Wang;E. Jenwitheesuk;R. Samudrala;J. Mittler
耐药性是成功治疗HIV-1感染的主要障碍。基因型检测广泛用于提供耐药性的间接证据，但这些检测的性能参差不齐。我们使用从斯坦福 HIV 耐药数据库获得的数据，使用标准逐步线性回归构建了 7 种蛋白酶抑制剂和 10 种逆转录酶抑制剂的耐药模型。我们通过留一实验和独立数据集上的测试来评估这些模型。我们的线性模型在两项测试中均优于其他公开的基因型解释算法，包括决策树、支持向量机和四种基于规则的算法（HIVdb、VGI、ANRS 和 Rega）。有趣的是，尽管没有任何关于蛋白酶或逆转录酶中不同残基之间相互作用的术语，我们的模型仍然表现良好。由此产生的线性模型易于理解，并且可能有助于选择联合治疗方案。
Drug resistance is a major obstacle to the successful treatment of HIV-1 infection. Genotypic assays are used widely to provide indirect evidence of drug resistance, but the performance of these assays has been mixed. We used standard stepwise linear regression to construct drug resistance models for seven protease inhibitors and 10 reverse transcriptase inhibitors using data obtained from the Stanford HIV drug resistance database. We evaluated these models by hold-one-out experiments and by tests on an independent dataset. Our linear model out-performed other publicly available genotypic interpretation algorithms, including decision tree, support vector machine and four rules-based algorithms (HIVdb, VGI, ANRS and Rega) under both tests. Interestingly, our model did well despite the absence of any terms for interactions between different residues in protease or reverse transcriptase. The resulting linear models are easy to understand and can potentially assist in choosing combination therapy regimens.
专家 HIV 1 型基因型解释的准确性、精密度和一致性：国际比较（GUESS 研究）。
DOI：
10.1086/430706
发表时间：
2005
期刊：
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
影响因子：
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作者：
Zolopa,AndrewR;Lazzeroni,LauraC;Rinehart,Alex;Vezinet,FrançoiseBrun;Clavel,François;Collier,Ann;Conway,Brian;Gulick,RoyM;Holodniy,Mark;Perno,Carlo-Frederico;Shafer,RobertW;Richman,DouglasD;Wainberg,MarkA;Kuritzkes,Daniel
通讯作者：
Kuritzkes,Daniel