Shrabanti Chowdhury

DAGBagM learns DAGs with mixed continuous and binary variables to identify prognostic protein biomarkers in ovarian cancer.

The paper introduces Tweedie models for differential expression analysis in scRNA-seq, showing improved performance over existing methods across platforms.

This paper introduces LRnetST, a statistical method for inferring ligand-receptor interaction networks from spatial transcriptomic data of ovarian tumors, revealing common and distinct interactions across samples.

An ensemble of six imputation methods, built through the NCI-CPTAC Proteogenomics DREAM Challenge, fills missing values in labeled LC-MS/MS proteomics data, helping most for low-abundance proteins.