Xinlei Mi

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 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.