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DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions

机译：DL-ADR：一种新型的深度学习模型，用于将基因组变异分类为药物不良反应

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Background Genomic variations are associated with the metabolism and the occurrence of adverse reactions of many therapeutic agents. The polymorphisms on over 2000 locations of cytochrome P450 enzymes (CYP) due to many factors such as ethnicity, mutations, and inheritance attribute to the diversity of response and side effects of various drugs. The associations of the single nucleotide polymorphisms (SNPs), the internal pharmacokinetic patterns and the vulnerability of specific adverse reactions become one of the research interests of pharmacogenomics. The conventional genomewide association studies (GWAS) mainly focuses on the relation of single or multiple SNPs to a specific risk factors which are a one-to-many relation. However, there are no robust methods to establish a many-to-many network which can combine the direct and indirect associations between multiple SNPs and a serial of events (e.g. adverse reactions, metabolic patterns, prognostic factors etc.). In this paper, we present a novel deep learning model based on generative stochastic networks and hidden Markov chain to classify the observed samples with SNPs on five loci of two genes (CYP2D6 and CYP1A2) respectively to the vulnerable population of 14 types of adverse reactions. Methods A supervised deep learning model is proposed in this study. The revised generative stochastic networks (GSN) model with transited by the hidden Markov chain is used. The data of the training set are collected from clinical observation. The training set is composed of 83 observations of blood samples with the genotypes respectively on CYP2D6*2, *10, *14 and CYP1A2*1C, *1?F. The samples are genotyped by the polymerase chain reaction (PCR)?method. A hidden Markov chain is used as the transition operator to simulate the probabilistic distribution. The model can perform learning at lower cost compared to the conventional maximal likelihood method because the transition distribution is conditional on the previous state of the hidden Markov chain. A least square loss (LASSO) algorithm and a k-Nearest Neighbors (kNN) algorithm are used as the baselines for comparison and to evaluate the performance of our proposed deep learning model. Results There are 53 adverse reactions reported during the observation. They are assigned to 14 categories. In the comparison of classification accuracy, the deep learning model shows superiority over the LASSO and kNN model with a rate over 80?%. In the comparison of reliability, the deep learning model shows the best stability among the three models. Conclusions Machine learning provides a new method to explore the complex associations among genomic variations and multiple events in pharmacogenomics studies. The new deep learning algorithm is capable of classifying various SNPs to the corresponding adverse reactions. We expect that as more genomic variations are added as features and more observations are made, the deep learning model can improve its performance and can act as a black-box but reliable verifier for other GWAS studies.
机译：背景基因组变异与许多治疗药物的代谢和不良反应的发生有关。由于种族，突变和遗传等许多因素，细胞色素P450酶（CYP）超过2000个位置的多态性归因于各种药物的反应多样性和副作用。单核苷酸多态性（SNPs）的关联，内部药代动力学模式和特定不良反应的脆弱性成为药物基因组学的研究兴趣之一。常规的全基因组关联研究（GWAS）主要关注单个或多个SNP与特定危险因素之间的关系，这些危险因素是一对多的关系。但是，目前尚没有建立多对多网络的可靠方法，该网络可以结合多个SNP与一系列事件（例如不良反应，代谢模式，预后因素等）之间的直接和间接关联。在本文中，我们提出了一种基于生成随机网络和隐马尔可夫链的新型深度学习模型，以将分别在两个基因（CYP2D6和CYP1A2）的五个基因座上的SNPs观察到的样本分类为14种不良反应的脆弱人群。方法本研究提出了一种监督式深度学习模型。使用修正的生成随机网络（GSN）模型，该模型由隐马尔可夫链传递。训练集的数据是从临床观察中收集的。训练集由83个分别具有CYP2D6 * 2，* 10，* 14和CYP1A2 * 1C，* 1？F基因型的血样观察值组成。通过聚合酶链反应（PCR）方法对样品进行基因分型。隐藏的马尔可夫链用作过渡算子，以模拟概率分布。与传统的最大似然方法相比，该模型可以以较低的成本执行学习，因为过渡分布取决于隐马尔可夫链的先前状态。最小平方损失（LASSO）算法和k最近邻（kNN）算法用作比较的基准，并评估了我们提出的深度学习模型的性能。结果观察期间报告了53种不良反应。它们被分为14个类别。在分类精度的比较中，深度学习模型显示出优于LASSO和kNN模型的优势，比率超过80％。在可靠性比较中，深度学习模型显示了三种模型中最佳的稳定性。结论机器学习为探索药物基因组学研究中基因组变异与多个事件之间的复杂关联提供了一种新方法。新的深度学习算法能够将各种SNP分类为相应的不良反应。我们期望随着功能的增加而增加更多的基因组变异并进行更多的观察，深度学习模型可以提高其性能，并且可以作为其他GWAS研究的黑匣子但可靠的验证者。

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