Radha Krishna Murthy Karuturi

NetAct constructs core transcription factor regulatory networks using transcriptomics data and literature databases, outperforming existing methods in network inference.

The paper develops bioinformatics workflows to analyze genomic data from patient-derived xenografts, addressing challenges like separating mouse and human reads and identifying somatic mutations without paired normal DNA.

The paper develops a tool to guide scRNA-seq experimental design and analysis for cell type identification, showing that Seurat or Simlr work well with high marker gene fold changes.

MultiDCoX identifies differentially co-expressed gene sets influenced by multiple factors in transcriptomic data, demonstrating effectiveness through simulated data and breast cancer analysis.