Xilin Shen

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.