Haidong Yi

RSVrecon is a bioinformatics pipeline that enhances RSV surveillance by automating genotyping and reporting clinically important mutations, providing user-friendly results for clinical researchers.

This paper provides a computational protocol for automated CAZyme annotation in microbiome sequencing data using dbCAN, offering publication-quality results and faster local execution.

This paper benchmarks differential abundance methods to identify condition-specific cell subpopulations in multi-sample single-cell datasets, finding dataset-specific recommendations for method usage.

scLKME generates multi-cellular sample embeddings from single-cell data by using a landmark-based kernel mean embedding method, outperforming existing techniques in robustness and accuracy.

CytoSet predicts clinical outcomes from cytometry data using set-based deep learning, achieving state-of-the-art performance and robustness across patient samples.