Beijing Institute of Pharmacology and Toxicology, State Key Laboratory of Toxicology and Medical Countermeasures, Beijing, 100850, China.
School of Instrumentation Science and Opto-Electronics Engineering and Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100083, China.
BMC Bioinformatics. 2021 Jan 7;22(1):17. doi: 10.1186/s12859-020-03915-6.
Querying drug-induced gene expression profiles with machine learning method is an effective way for revealing drug mechanism of actions (MOAs), which is strongly supported by the growth of large scale and high-throughput gene expression databases. However, due to the lack of code-free and user friendly applications, it is not easy for biologists and pharmacologists to model MOAs with state-of-art deep learning approach.
In this work, a newly developed online collaborative tool, Genetic profile-activity relationship (GPAR) was built to help modeling and predicting MOAs easily via deep learning. The users can use GPAR to customize their training sets to train self-defined MOA prediction models, to evaluate the model performances and to make further predictions automatically. Cross-validation tests show GPAR outperforms Gene set enrichment analysis in predicting MOAs.
GPAR can serve as a better approach in MOAs prediction, which may facilitate researchers to generate more reliable MOA hypothesis.
使用机器学习方法查询药物诱导的基因表达谱是揭示药物作用机制（MOA）的有效方法，这得到了大规模、高通量基因表达数据库的发展的有力支持。然而，由于缺乏无代码和用户友好的应用程序，生物学家和药理学家不容易使用最先进的深度学习方法来构建 MOA 模型。
在这项工作中，开发了一个新的在线协作工具，称为遗传谱-活性关系（GPAR），用于通过深度学习轻松构建和预测 MOA。用户可以使用 GPAR 自定义训练集来训练自定义的 MOA 预测模型，评估模型性能并自动进行进一步预测。交叉验证测试表明，GPAR 在预测 MOA 方面优于基因集富集分析。
GPAR 可以作为一种更好的 MOA 预测方法，这可能有助于研究人员生成更可靠的 MOA 假设。
