Motivation: Omics features, often measured by high-throughput technologies, combined with clinical features, significantly impact the understanding of many complex human diseases. Integrating key omics biomarkers with clinical risk factors is essential for elucidating disease mechanisms, advancing early diagnosis, and enhancing precision medicine. However, the high dimensionality and intricate associations between disease outcomes and omics profiles present substantial analytical challenges. Results: We propose a high-dimensional feature importance test (HiFIT) framework to address these challenges. Specifically, we develop an ensemble data-driven biomarker identification tool, Hybrid Feature Screening (HFS), to construct a candidate feature set for downstream machine learning models. The pre-screened candidate features from HFS are further refined using a computationally efficient permutation-based feature importance test employing machine learning methods to flexibly model the potential complex associations between disease outcomes and molecular biomarkers. Through extensive numerical simulation studies and practical applications to microbiome-associated weight changes following bariatric surgery, as well as the examination of gene-expression-associated kidney pan-cancer survival data, we demonstrate HiFIT’s superior performance in both outcome prediction and feature importance identification. Availability and implementation: An R package implementing the HiFIT algorithm is available on GitHub (https://github.com/BZou-lab/HiFIT).

The integration of drug molecular representations into predictive models for Drug Response Prediction (DRP) is a standard procedure in pharmaceutical research and development. However, the comparative effectiveness of combining these representations with genetic profiles for DRP remains unclear. This study conducts a comprehensive evaluation of the efficacy of various drug molecular representations employing cutting-edge machine learning models under various experimental settings. Our findings reveal that the inclusion of molecular representations from either PubChem fingerprints or SMILES can significantly enhance the performance of DRPs when used in conjunction with deep learning models. However, the optimal choice of drug molecular representation can vary depending on the predictive model and the specific DRP task. The insights derived from our study offer useful guidance on selecting the most suitable drug molecular representations for constructing efficient predictive models for DRPs, aiding for drug repurposing, personalized medicine, and new drug discovery.

We aimed to develop a robust framework to model the complex association between clinical features and traumatic brain injury (TBI) risk in children under age two, and identify significant features to derive clinical decision rules for triage decisions. In this retrospective study, four frequently used machine learning models, i.e., support vector machine (SVM), random forest (RF), deep neural network (DNN), and XGBoost (XGB), were compared to identify significant clinical features from 24 input features associated with the TBI risk in children under age two under the permutation feature importance test (PermFIT) framework by using the publicly available data set from the Pediatric Emergency Care Applied Research Network (PECARN) study. The prediction accuracy was determined by comparing the predicted TBI status with the computed tomography (CT) scan results since CT scan is the gold standard for diagnosing TBI. At a significance level of \(p = 0.05\), DNN, RF, XGB, and SVM identified 9, 1, 2, and 4 significant features, respectively. In a comparison of accuracy (Accuracy), the area under the curve (AUC), and the precision-recall area under the curve (PR-AUC), the permutation feature importance test for DNN model was the most powerful framework for identifying significant features and outperformed other methods, i.e., RF, XGB, and SVM, with Accuracy, AUC, and PR-AUC as 0.915, 0.794, and 0.974, respectively. These results indicate that the PermFIT-DNN framework robustly identifies significant clinical features associated with TBI status and improves prediction performance. The findings could be used to inform the development of clinical decision tools designed to inform triage decisions.