Maalavika Pillai

The paper develops a model to distinguish biologically relevant from spurious transcription factor binding events using distance thresholds and validates it with RNA Polymerase II recruitment and mRNA expression data.

The paper uses multi-modal transcriptomic data to show basal breast cancer exhibits hybrid epithelial/mesenchymal states and higher phenotypic heterogeneity compared to luminal breast cancer.

The paper constructs cell-state transition landscapes in melanoma to quantify dynamics of drug resistance, revealing alternative paths like dedifferentiation and hyper-pigmentation, and identifies optimal therapeutic strategies.

SOAR provides a curated spatial transcriptomics database and analysis tools to facilitate disease mechanism studies and drug discovery.

The paper models a transcription factor network in melanoma to predict phenotypic heterogeneity and identify master regulators driving tumor plasticity and therapeutic resistance.