Xiangwen Ji

The paper develops a Cell Size Score (CSS) to predict cell size from gene expression and validates its utility in disease and physiological contexts.

The paper evaluates somatic contamination in germline genomes using WES and WGS data from UK Biobank, TCGA, and 1000 Genomes Project, revealing limitations in current filtering methods and the impact of somatic mutations on genetic studies.

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 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.

This paper introduces a computational framework, GSGP, to decipher somatic mutational signatures and identifies novel aetiologies, including an RNA-derived signature in colorectal cancer.