Edward S.C. Shih

The paper presents an ensemble model that improves predictions of perceptual similarity between odor mixtures using a curated dataset and collaborative challenge results.

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 identifies UTX/KDM6A's role in tumor maintenance through DNA replication and repair pathways, showing that targeting the protein, not just its enzymatic activity, could be a therapeutic strategy.

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 organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

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