Baiming Zou

The paper develops deepHTL, a deep learning method to detect treatment effect heterogeneity in lung transplant outcomes, finding significant variation in benefits based on recipient characteristics.

DEPICT predicts drug-induced transcriptional responses from gene expression and drug data, outperforming existing methods and enabling drug screening and synergy discovery.

The paper develops GARD, a pipeline using genomic data and large language models to identify drug repurposing candidates for head and neck cancer, finding both known and emerging therapeutic options.

This paper introduces SurvDNN, a deep learning framework for survival outcome modeling in complex diseases, which outperforms existing methods in biomarker identification and prediction.

This paper introduces a deep neural network-based two-part model for semi-continuous data and demonstrates its improved performance over existing methods.

This paper introduces HiFIT, a framework for high-dimensional biomarker identification that improves disease prediction and feature importance detection using machine learning.

This paper introduces a deep learning semiparametric regression method to adjust complex confounding structures in observational studies, showing reduced bias and mean squared error in parameter estimates.

This paper introduces PermFIT, a permutation-based method to assess feature importance in black-box models, showing it improves prediction accuracy and identifies biomarkers in cancer and BMI data.