A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants.
A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants.
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一种新的基于机器学习的药物基因组变异计算功能评估方法。
DOI：
10.1186/s40246-021-00352-1
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发表时间：
2021-08-09
期刊：
影响因子：
4.5
通讯作者：
Patrinos GP
中科院分区：
文献类型：
--
作者：
Pandi MT;Koromina M;Tsafaridis I;Patsilinakos S;Christoforou E;van der Spek PJ;Patrinos GP
药物基因组学领域关注的是一个人的基因组如何影响他或她对特定剂量的特定药物的反应。其主要目的是利用这些信息来指导和个性化治疗，使患者的临床利益最大化，风险最小化，从而实现个性化药物的承诺。基因组测序技术的进步，加上改进的计算方法的开发，以有效地分析产生的海量数据，使得快速而廉价地对患者的基因组进行测序，从而使其纳入临床常规实践成为现实的可能性。这项研究充分利用了参与药物代谢和运输的基因在功能水平上的SNV特征，以训练一个分类器，该分类器将根据对蛋白质功能的预期影响对新的变体进行分类。该分类基于通过使用递归特征消除过程来选择的计算机预测和/或保护中的可用分数。为此，利用了关于190种药物变量的信息，以及4种机器学习算法，即AdaBoost、XGBoost、多项Logistic回归和随机森林，通过5次交叉验证评估了它们的性能。所有模型在得出知情结论方面都取得了相似的表现，其中RF模型获得了最高的准确性(85%，95%CI：0.79，0.90)，以及改善的总体性能(精度85%，敏感性84%，特异性94%)，并用于后续分析。当应用于真实世界的WGS数据时，选择的RF模型识别出2个错义变体，预计会导致功能蛋白质减少，1个增加。正如预期的那样，当将该方法用于从编码区定向重新测序获得的NGS数据时，强调了更多的变体。具体地说，在156个有充分注释信息的变体中，71个变体被归类为“功能降低”，41个变体被归类为“无”功能蛋白质，1个变体被归类为“功能增强”。总体而言，建议的基于RF的分类模型有望导致极其有用的变体优先级排序，并作为一种评分工具，在药物基因组学和个性化医学领域具有有趣的临床应用。网上版载有补充材料，可在10.1186/s40246-021-00352-1查阅。
The field of pharmacogenomics focuses on the way a person’s genome affects his or her response to a certain dose of a specified medication. The main aim is to utilize this information to guide and personalize the treatment in a way that maximizes the clinical benefits and minimizes the risks for the patients, thus fulfilling the promises of personalized medicine. Technological advances in genome sequencing, combined with the development of improved computational methods for the efficient analysis of the huge amount of generated data, have allowed the fast and inexpensive sequencing of a patient’s genome, hence rendering its incorporation into clinical routine practice a realistic possibility. This study exploited thoroughly characterized in functional level SNVs within genes involved in drug metabolism and transport, to train a classifier that would categorize novel variants according to their expected effect on protein functionality. This categorization is based on the available in silico prediction and/or conservation scores, which are selected with the use of recursive feature elimination process. Toward this end, information regarding 190 pharmacovariants was leveraged, alongside with 4 machine learning algorithms, namely AdaBoost, XGBoost, multinomial logistic regression, and random forest, of which the performance was assessed through 5-fold cross validation. All models achieved similar performance toward making informed conclusions, with RF model achieving the highest accuracy (85%, 95% CI: 0.79, 0.90), as well as improved overall performance (precision 85%, sensitivity 84%, specificity 94%) and being used for subsequent analyses. When applied on real world WGS data, the selected RF model identified 2 missense variants, expected to lead to decreased function proteins and 1 to increased. As expected, a greater number of variants were highlighted when the approach was used on NGS data derived from targeted resequencing of coding regions. Specifically, 71 variants (out of 156 with sufficient annotation information) were classified as to “Decreased function,” 41 variants as “No” function proteins, and 1 variant in “Increased function.” Overall, the proposed RF-based classification model holds promise to lead to an extremely useful variant prioritization and act as a scoring tool with interesting clinical applications in the fields of pharmacogenomics and personalized medicine. The online version contains supplementary material available at 10.1186/s40246-021-00352-1.
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