Dan Chang

The paper identifies treatment response-associated cell subpopulations in autoimmune diseases by integrating single-cell and bulk data from IBD and PsO patients, revealing key pathways and cell types involved in response or non-response.

The paper identifies two molecular subtypes of hidradenitis suppurativa using transcriptomic data and validates their association with drug response.

scCompare provides a web tool for comparing scRNA-seq datasets across autoimmune diseases to identify and analyze differential gene expression and pathogenesis.

This study evaluates the impact of tissue- and blood-derived reference matrices on deconvolving bulk transcriptomic datasets, finding tissue-derived matrices perform better for tissue samples but not for blood.

This paper introduces DeepBGC, a deep learning method for improved biosynthetic gene cluster prediction, uncovering previously undetectable clusters with potential novel biological activities.