Tony Chien-Yen Kuo

The paper introduces SIGnature, a framework for scoring gene importance using attributions from scRNA-seq foundation models, and demonstrates its utility in identifying novel associations of the MS1 signature with hyperinflammatory conditions.

PaSCient learns patient-level disease representations from scRNA-seq data, enabling disease classification and downstream analyses like dimensionality reduction and patient subgroup discovery.

The paper introduces SCimilarity, a metric learning framework for querying and comparing single-cell RNA-seq data across the human body, revealing commonalities in fibrosis-associated macrophages.

ARPEGGIO provides an automated workflow for analyzing epigenetic data in polyploids, improving accuracy and reproducibility in DNA methylation analysis of allopolyploids.

This study compares RNA-seq expression quantification methods for allopolyploids, finding that subgenome-classification approaches significantly reduce error rates compared to standard methods.