Kun Wang

This paper develops DECODEM to predict chemotherapy response by integrating deconvolved expression patterns of immune, stromal, and tumor cells in breast cancer, finding that cell-type-specific expression is predictive of response.

The paper compares gene expression in primary and metastatic tumors to their origin and target tissues, finding metastases more resemble target tissues and show reduced cancer hallmark activity.

IMMClock predicts immune age of T cells and NK cells using gene expression, showing immune aging is more linked to cellular aging than differentiation and is accelerated in diseases.

The paper identifies resistance-deactivated interactions (RDIs) as predictive biomarkers for ICB response in melanoma by analyzing transcriptomics data from multiple cohorts.

The paper infers tumor immune status and predicts immunotherapy response from blood scRNA-Seq data in HNSCC patients.

PertKGE improves compound-protein interaction prediction using knowledge graph embedding of perturbation transcriptomics, outperforming baselines in cold-start settings and identifying novel therapeutic targets.

This study predicts tissue-specific gene expression from whole blood transcriptome using GTEx data, finding that blood expression can predict expression in many tissues and improve disease prediction.

The paper introduces SNiPage, a framework to identify SNP-age interactions in transcriptional control, finding 637 TF-SNP-Gene triplets across 25 tissues linked to aging and age-related diseases.

The paper develops CODEFACS and LIRICS to deconvolve bulk gene expression and identify clinically relevant ligand-receptor interactions in tumors, finding specific T-cell interactions linked to anti-PD1 response.