Mining complex genotypic features for predicting HIV-1 drug resistance
Mining complex genotypic features for predicting HIV-1 drug resistance
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DOI：
10.1093/bioinformatics/btm353
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发表时间：
2007-09-15
期刊：
影响因子：
5.8
通讯作者：
Tsuda, Koji
中科院分区：
文献类型：
--
作者：
Saigo, Hiroto;Uno, Takeaki;Tsuda, Koji
动机：人类免疫缺陷病毒1型（HIV-1）在人体内进化，其暴露于药物通常会引起突变，增强对药物的抵抗力。为了为个体患者设计有效的药物治疗，重要的是基于基因型数据准确预测耐药性。值得注意的是，抗性不仅仅是所有突变效应的简单总和。结构生物学研究表明，突变的关联是至关重要的：即使突变A或B单独不影响耐药性，当两个突变一起发生时，也可能发生显著变化。线性回归方法不能考虑关联，而决策树方法只能揭示有限的关联。核方法和神经网络隐式地使用所有可能的关联进行预测，但不能选择显式associations explicitly.Results：我们的方法，项集提升，在突变的幂集的完整空间中进行线性回归。它实现了一个前向特征选择过程，在每次迭代中，一个突变组合是由一个有效的分支和定界搜索。该方法使用所有可能的组合，并且显式地显示突出的关联。在实验中，我们的方法特别适用于预测核苷酸逆转录酶抑制剂（NRTI）的耐药性。此外，它成功地恢复了生物学文献中已知的许多突变关联。
Motivation: Human immunodeficiency virus type 1 (HIV-1) evolves in human body, and its exposure to a drug often causes mutations that enhance the resistance against the drug. To design an effective pharmacotherapy for an individual patient, it is important to accurately predict the drug resistance based on genotype data. Notably, the resistance is not just the simple sum of the effects of all mutations. Structural biological studies suggest that the association of mutations is crucial: even if mutations A or B alone do not affect the resistance, a significant change might happen when the two mutations occur together. Linear regression methods cannot take the associations into account, while decision tree methods can reveal only limited associations. Kernel methods and neural networks implicitly use all possible associations for prediction, but cannot select salient associations explicitly.Results: Our method, itemset boosting, performs linear regression in the complete space of power sets of mutations. It implements a forward feature selection procedure where, in each iteration, one mutation combination is found by an efficient branch- and- bound search. This method uses all possible combinations, and salient associations are explicitly shown. In experiments, our method worked particularly well for predicting the resistance of nucleotide reverse transcriptase inhibitors ( NRTIs). Furthermore, it successfully recovered many mutation associations known in biological literature.