On using AutoML to Predict Clinical Outcomes

AutoML allows users to create high-quality machine learning models to solve real-world problems without much coding. Recently, Automl has been used in machine learning competitions such as Kaggle and showed excellent performance. The purpose of this study is to investigate whether AutoML can be utilized for biologists who have little experience with machine learning can use AutoML to gain insights from their data. In this study, I will re-analyze a case-control gene expression data set with open-source AutoML frameworks. I will compare the frameworks on their performance on creating a predictive model for disease using biomarkers from the expression data. I will demonstrate how to keep track of models and their hyperparameters using MLflow. Finally, I will attempt to gain insights by integrating information from explainable AI tools such as DALEX and biological pathways for gene set enrichment analysis