Hongyi Xin

RegDesign-Bench evaluates computational methods for designing cell-type-specific regulatory DNA, showing that performance on individual metrics does not guarantee overall effectiveness.

The paper benchmarks domain segmentation algorithms for spatial transcriptomics data, showing they fail to identify biologically defined niches due to noise and highlights the need for specialized methods.

AAB-seq identifies antigen-binding antibodies with affinity information from single-cell BCR sequencing in immunized mice.

This paper introduces GeCCo, a method for cell type identification in single-cell transcriptomics that improves hierarchical consistency by constructing identities based on gene programs.

GAIA provides a framework for analyzing single-cell and spatial transcriptomic data by reconciling discrete and continuous gene expression variation through information geometry.

VeloTrace unifies RNA velocity and trajectory inference using NeuralODEs, improving accuracy and coherence in single-cell transcriptomic analysis.

This paper introduces Bidirectional Beam Search for efficient trace reconstruction in DNA storage systems, achieving high accuracy and speed on real datasets.

TIST integrates spatial transcriptomics data with histopathological images to improve spatial cluster identification and gene expression analysis.

This paper introduces GMM-Demux, a method to identify and remove multiplets in scRNA-seq data, showing improved accuracy and error reduction.

CITE-sort improves cell type classification in CITE-seq by addressing artificial cell types caused by multiplets, achieving better clustering performance than existing methods.

The paper introduces a new seeding framework for read mapping that reduces seed frequencies and improves efficiency by using context-aware seeds with confidence radii.

BREM-SC is a Bayesian model that jointly clusters single-cell transcriptomic and proteomic data, improving accuracy and quantifying clustering uncertainty.

BAMM-SC clusters droplet-based scRNA-seq data from multiple individuals by accounting for batch effects and heterogeneity, improving accuracy over existing methods.

The paper introduces LEAP, a generalized algorithm for approximate string matching that outperforms existing methods in speed under various scoring schemes.