Learning Robust Multilabel Sample Specific Distances for Identifying HIV-1 Drug Resistance
Learning Robust Multilabel Sample Specific Distances for Identifying HIV-1 Drug Resistance
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DOI：
10.1089/cmb.2019.0329
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发表时间：
2019-11-15
影响因子：
1.7
通讯作者：
Nie, Feiping
中科院分区：
文献类型：
--
作者：
Brand, Lodewijk;Yang, Xue;Nie, Feiping
艾滋病是由艾滋病毒引起的一种综合症。在艾滋病的发展过程中，患者的免疫系统被削弱，这增加了患者对感染和疾病的易感性。虽然抗逆转录病毒药物可以有效地抑制艾滋病毒，但病毒变异非常快，可能对治疗产生抗药性。此外，该病毒还可以通过突变对目前未使用的其他治疗方法产生耐药性，这在临床研究界被称为交叉耐药性。由于单个HIV毒株可以对多种药物产生耐药性，因此这个问题自然被表示为多标签分类问题。鉴于这种多标签关系，传统的单标签分类方法往往无法有效识别特定病毒突变后可能产生的耐药性。在这项工作中，我们提出了一种新的多标签鲁棒样本特异性距离（RSSD）方法来识别多类HIV耐药性。我们的方法是新颖的，因为它可以说明逆转录酶（RT）序列对给定药物核苷类似物的耐药性的相对强度，并学习所有耐药性的距离度量。为了学习所提出的RSSD，我们制定了一个学习目标，最大化一些l（1）-范数距离的总和的比率，这是很难解决的一般。为了解决这个优化问题，我们推导出一个有效的，非贪婪的迭代算法，严格证明收敛。我们的新方法已经在一个公开的HIV 1型耐药数据集上得到了验证，该数据集包含600多个RT序列和5个核苷类似物。我们比较了我们的方法对几个国家的最先进的多标签分类方法，实验结果表明，我们提出的方法的有效性。
AIDS is a syndrome caused by the HIV. During the progression of AIDS, a patient's immune system is weakened, which increases the patient's susceptibility to infections and diseases. Although antiretroviral drugs can effectively suppress HIV, the virus mutates very quickly and can become resistant to treatment. In addition, the virus can also become resistant to other treatments not currently being used through mutations, which is known in the clinical research community as cross-resistance. Since a single HIV strain can be resistant to multiple drugs, this problem is naturally represented as a multilabel classification problem. Given this multilabel relationship, traditional single-label classification methods often fail to effectively identify the drug resistances that may develop after a particular virus mutation. In this work, we propose a novel multilabel Robust Sample Specific Distance (RSSD) method to identify multiclass HIV drug resistance. Our method is novel in that it can illustrate the relative strength of the drug resistance of a reverse transcriptase (RT) sequence against a given drug nucleoside analog and learn the distance metrics for all the drug resistances. To learn the proposed RSSDs, we formulate a learning objective that maximizes the ratio of the summations of a number of l(1)-norm distances, which is difficult to solve in general. To solve this optimization problem, we derive an efficient, nongreedy iterative algorithm with rigorously proved convergence. Our new method has been verified on a public HIV type 1 drug resistance data set with over 600 RT sequences and five nucleoside analogs. We compared our method against several state-of-the-art multilabel classification methods, and the experimental results have demonstrated the effectiveness of our proposed method.