Jin‐Hong Du

The paper shows that transcriptional perturbations have structured effects beyond a global trend, with specific patterns transferable across datasets.

The paper introduces causarray, a causal inference framework for analyzing genomic data under unmeasured confounders, and applies it to identify causal effects in autism and Alzheimer's disease studies.

The paper introduces a statistical framework for proteomic data analysis that combines imputation with debiasing to improve inference and reduce false positives in both single-cell and bulk data.

This paper introduces VITAE, a deep learning method for trajectory inference in single-cell genomics that improves accuracy and uncertainty quantification across datasets.

This paper introduces scVAEIT, a probabilistic variational autoencoder for integrating and imputing multimodal single-cell data with mosaic measurements, showing improved performance across unseen cell types and tissues.