Tsai‐Min Chen

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

This paper develops machine learning models to predict small-molecule drug FDA approvability and clinical trial success using molecular structure and physicochemical properties, significantly reducing attrition rates.

The paper develops a deep learning model for detecting and classifying cardiac arrhythmias using ECG data, achieving high accuracy in a competition setting.