Bo Yuan

The paper harmonizes single-cell perturbation datasets and introduces E-statistics for analyzing perturbation effects across diverse data sources.

The paper introduces ERTransformer, a large language model that generates T-cell receptors with desired epitope-binding properties, showing high functional similarity to natural TCRs.

This study evaluates ML-based phenotyping's robustness to label noise using VCDR data, showing improved genetic discovery and PRS predictive power with denoising.

This paper uses deep learning to predict pancreatic cancer risk from clinical disease trajectories in two large datasets, achieving higher accuracy than models without temporal information.

The paper introduces a hybrid machine learning approach for perturbation biology to identify combinatorial therapeutic candidates using a melanoma cell line dataset.