Genentech Inc, South San Francisco, California, USA.
CPT Pharmacometrics Syst Pharmacol. 2021 May;10(5):478-488. doi: 10.1002/psp4.12621. Epub 2021 May 2.
Although there is increased interest in utilizing machine learning (ML) to support drug development, technical hurdles associated with complex algorithms have limited widespread adoption. In response, we have developed Pharm-AutoML, an open-source Python package that enables users to automate the construction of ML models and predict clinical outcomes, especially in the context of pharmacological interventions. In particular, our approach streamlines tedious steps within the ML workflow, including data preprocessing, model tuning, model selection, results analysis, and model interpretation. Moreover, our open-source package helps to identify the most predictive ML pipeline among defined search spaces by selecting the best data preprocessing strategy and tuning the ML model hyperparameters. The package currently supports multiclass classification tasks, and additional functions are being developed. Using a set of five publicly available biomedical datasets, we show that our Pharm-AutoML outperforms other ML frameworks, including H2O with default settings, by demonstrating improved predictive accuracy of classification. We further illustrate how model interpretation methods can be utilized to help explain the fine-tuned ML pipeline to end users. Pharm-AutoML provides both novice and expert users improved clinical predictions and scientific insights.
尽管人们越来越有兴趣利用机器学习 (ML) 来支持药物开发，但与复杂算法相关的技术障碍限制了其广泛采用。有鉴于此，我们开发了 Pharm-AutoML，这是一个开源的 Python 软件包，使用户能够自动化构建 ML 模型并预测临床结果，特别是在药理学干预的背景下。具体来说，我们的方法简化了 ML 工作流程中的繁琐步骤，包括数据预处理、模型调整、模型选择、结果分析和模型解释。此外，我们的开源软件包通过选择最佳的数据预处理策略和调整 ML 模型超参数，有助于在定义的搜索空间中识别最具预测性的 ML 管道。该软件包目前支持多类分类任务，并且正在开发其他功能。我们使用了五组公开可用的生物医学数据集，结果表明，我们的 Pharm-AutoML 优于其他 ML 框架，包括默认设置的 H2O，通过展示分类预测精度的提高来证明这一点。我们进一步说明了如何利用模型解释方法来帮助向最终用户解释微调后的 ML 管道。Pharm-AutoML 为新手和专家用户提供了改进的临床预测和科学见解。
