Xiangchun Li

OmniNA is a foundation model for nucleotide sequences trained on diverse species data, achieving performance across 17 tasks and offering insights into mutation effects on DNA/RNA processing.

Reformer predicts protein-RNA binding affinity from sequence with single-base resolution, showing high accuracy and discovering novel binding motifs.

This paper introduces a scalable batch-correction method for integrating large-scale single-cell transcriptomes and shows it matches state-of-the-art performance on 18 million cells from the Human Cell Atlas.

The paper introduces tGPT, a generative model for integrating large-scale single-cell transcriptomes, showing its effectiveness in clustering, trajectory inference, and linking gene patterns to clinical outcomes.

The paper introduces iSEEEK, a method for integrating large-scale single-cell transcriptomes using gene rankings, showing good clustering and transferability across datasets.

This study identifies novel somatic mutations and altered mutation frequencies in Chinese colorectal cancer patients using whole exome sequencing, highlighting differences from TCGA data.