Junyi Li

MEAHNE predicts miRNA-disease associations using a heterogeneous network model, achieving high accuracy and verifying predictions against databases.

The paper introduces an explainable t-SNE method for scRNA-seq data that improves clustering performance and interpretability while addressing limitations of traditional t-SNE.

This study performs genome assembly and transcriptome analysis of Microtus fortis to understand its resistance to Schistosoma japonicum infection, revealing differences in immune response timing and effectiveness compared to mice.

deSALT improves long RNA-seq read alignment by addressing sequencing errors and gene structure complexity through a two-pass graph-based approach.

deGSM constructs large de Bruijn graphs efficiently in limited memory, handling terabase-pair datasets on standard hardware.

rMETL detects mobile element insertions using long read realignment, improving sensitivity and reducing false positives in genomic studies.