Zhong Yezhao, Seoighe Cathal, Yang Haixuan
School of Mathematical & Statistical Sciences, University of Galway, Galway H91 TK33, Ireland.
Bioinform Adv. 2024 Jan 23;4(1):vbae009. doi: 10.1093/bioadv/vbae009. eCollection 2024.
Post-market unexpected Adverse Drug Reactions (ADRs) are associated with significant costs, in both financial burden and human health. Due to the high cost and time required to run clinical trials, there is significant interest in accurate computational methods that can aid in the prediction of ADRs for new drugs. As a machine learning task, ADR prediction is made more challenging due to a high degree of class imbalance and existing methods do not successfully balance the requirement to detect the minority cases (true positives for ADR), as measured by the Area Under the Precision-Recall (AUPR) curve with the ability to separate true positives from true negatives [as measured by the Area Under the Receiver Operating Characteristic (AUROC) curve]. Surprisingly, the performance of most existing methods is worse than a naïve method that attributes ADRs to drugs according to the frequency with which the ADR has been observed over all other drugs. The existing advanced methods applied do not lead to substantial gains in predictive performance.
We designed a rigorous evaluation to provide an unbiased estimate of the performance of ADR prediction methods: Nested Cross-Validation and a hold-out set were adopted. Among the existing methods, Kernel Regression (KR) performed best in AUPR but had a disadvantage in AUROC, relative to other methods, including the naïve method. We proposed a novel method that combines non-negative matrix factorization with kernel regression, called VKR. This novel approach matched or exceeded the performance of existing methods, overcoming the weakness of the existing methods.
Code and data are available on https://github.com/YezhaoZhong/VKR.
上市后意外药物不良反应（ADR）在经济负担和人类健康方面都带来了巨大成本。由于开展临床试验所需的高成本和时间，人们对能够帮助预测新药ADR的准确计算方法有着浓厚兴趣。作为一项机器学习任务，ADR预测因高度的类别不平衡而更具挑战性，并且现有方法未能成功平衡检测少数情况（ADR的真阳性）的要求，这一要求通过精确率-召回率曲线下面积（AUPR）来衡量，同时还需具备将真阳性与真阴性区分开的能力（通过受试者工作特征曲线下面积（AUROC）来衡量）。令人惊讶的是，大多数现有方法的性能比一种简单方法还要差，该简单方法根据ADR在所有其他药物中被观察到的频率将ADR归因于药物。所应用的现有先进方法并未在预测性能上带来显著提升。
我们设计了一项严格评估，以对ADR预测方法的性能提供无偏估计：采用了嵌套交叉验证和留出集。在现有方法中，核回归（KR）在AUPR方面表现最佳，但相对于其他方法，包括简单方法，在AUROC方面存在劣势。我们提出了一种将非负矩阵分解与核回归相结合的新方法，称为VKR。这种新方法达到或超过了现有方法的性能，克服了现有方法的弱点。
