论文标题
使用开放式TG-GATE和FAERS数据库对不良药物反应的深度学习预测
Deep Learning Prediction of Adverse Drug Reactions Using Open TG-GATEs and FAERS Databases
论文作者
论文摘要
随着人工智能（AI）的进步以及与健康相关的大数据的积累，利用机器学习技术来分析临床和OMICS元数据以评估药物发现过程中不良药物反应或事件（ADR）的可能性。在这里，我们描述了一种新的方法，该方法结合了来自开放式TG-GATES（毒理基因组学辅助毒性评估系统）和来自FAERS（FDA [FDA [食品和药物管理]不良事件报告）数据库中的ADR信息的基因表达谱图，以预测ADRS的可能性。我们使用深神经网络（DNN）生成了14个模型，以预测不同的ADR。在验证测试中，我们的模型达到了85.71％的平均准确性，这表明我们的方法成功，一致地预测了广泛药物的ADR。例如，我们在十二指肠溃疡的背景下描述了ADR模型。我们认为，我们的模型将有助于预测ADR的可能性，同时测试新型药物化合物，并且对药物发现研究人员很有用。
With the advancements in Artificial intelligence (AI) and the accumulation of healthrelated big data, it has become increasingly feasible and commonplace to leverage machine learning technologies to analyze clinical and omics metadata to assess the possibility of adverse drug reactions or events (ADRs) in the course of drug discovery. Here, we have described a novel approach that combined drug-induced gene expression profile from Open TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation Systems) and ADR occurrence information from FAERS (FDA [Food and Drug Administration] Adverse Events Reporting System) database to predict the likelihood of ADRs. We generated a total of 14 models using Deep Neural Networks (DNN) to predict different ADRs; in the validation tests, our models achieved a mean accuracy of 85.71%, indicating that our approach successfully and consistently predicted ADRs for a wide range of drugs. As an example, we have described the ADR model in the context of Duodenal ulcer. We believe that our models will help predict the likelihood of ADRs while testing novel pharmaceutical compounds, and will be useful for researchers in drug discovery.
