Yi Zhang

The paper evaluates 24 computational methods for predicting non-coding variant function and pathogenicity across four benchmark datasets, finding varying performance across different variant types.

This study combines manual annotation and deep learning to improve name entity recognition in biomedical texts, achieving high F1-scores for gene, variant, disease, and species entities.

Cancer SIGVAR is a tool for interpreting germline cancer variants using 21 and 48 criteria, achieving high consistency in classification across benchmark databases.