Jett P. Crowdis

The paper identifies immune spatial organization as a biomarker predicting metastasis risk in high-grade prostate cancer using computational pathology and H&E slides.

The paper introduces BEANIE, a nonparametric method to improve differential enrichment analysis in single-cell transcriptomics by accounting for sample-specific biology and robustness of signatures across cohorts.

This study analyzes structural variants in melanoma subtypes, identifying distinct genomic properties and molecular drivers that may inform therapeutic strategies.

This paper reports a case where platinum chemotherapy exposure was linked to the development of aggressive thyroid cancer through mutational signatures and genomic analysis.

This paper introduces a biologically informed deep learning model for prostate cancer classification and identifies molecular drivers of treatment resistance.

This study identifies transcriptional states in mCRPC that mediate treatment resistance, including AR splicing heterogeneity, TGF-β signaling, and immune infiltration patterns.

CoMut creates customizable comutation plots from user data to visualize genomic and phenotypic relationships in patient cohorts.