Hao Yuan

This paper quantifies sex imbalance in biomedical research by analyzing transcriptome samples and study metadata, revealing male-skewed representation in many disease areas.

ICePop links genetic risk to disease-relevant cellular states by analyzing single-cell transcriptomics data across multiple traits, identifying 1,684 disease-cell type associations.

The paper estimates species abundance using segregating sites from eDNA, finding a stronger correlation than eDNA concentration.

This paper introduces txt2onto 2.0, a method for annotating biomedical metadata with disease and tissue terms using interpretable machine learning, showing improved performance over its predecessor.

CONE generates context-specific gene embeddings from context-free interactomes to identify disease-associated genes using contextualized random walks and graph attention networks.

This paper introduces an unsupervised deep learning method to derive brain imaging phenotypes from MRI data, identifying new genetic loci associated with these endophenotypes.