SysML: adaptive recommendation system for heterogeneous biomedical data preprocessing and modeling workflows.

ABSTRACT: The rapid growth of high-dimensional omics datasets in biomedical research has created an urgent need for computational frameworks that are both robust and adaptable to diverse data complexities. Although a wide range of specialized tools and algorithms are available, researchers often rely on trial-and-error approaches to select suitable analytical workflows, compromising both efficiency and reproducibility. In this study, we systematically benchmarked hundreds of algorithms-preprocessing combinations across three common biomedical data challenges, including small sample sizes, missing values, and class imbalance. Our results show that tree-based models (e.g. Gradient Boosting Decision Tree, XGBoost, and Random Forest) consistently perform well in scenarios involving small-sample and miss

SUBMITTER: Zhao J 

PROVIDER: S-EPMC12536875 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature