Rui Fan

The paper identifies novel m6A readers that regulate human embryonic stem cell differentiation through comprehensive profiling of m6A readouts across multiple cell lines.

The paper introduces MedImg, a database integrating 1,622,956 medical images from 103 datasets across 14 modalities and 9 organs, to support deep learning research in medical image analysis.

The paper introduces an improved Ka/Ks ratio (iKa/Ks) that accounts for the non-neutral effects of synonymous mutations on miRNA regulation, showing better performance in detecting selection pressures than the conventional Ka/Ks ratio.

The paper introduces PIC, a deep learning model for predicting human protein essentiality, outperforming existing methods and quantifying essentiality across humans, cell lines, and mice orthologues.

The paper introduces BIC, a tool to measure single base importance in human mRNAs and lncRNAs, showing its utility in pathogenicity assessment, cancer prognosis, and SARS-CoV-2 transmissibility prediction.

The paper introduces WEAT, a weighted gene set enrichment tool that incorporates gene essentiality scores to improve functional analysis of gene lists.