Edward Michael Gertz

This paper develops a machine learning framework, EXPRESSO, to predict treatment response from tumor transcriptomics across multiple cancer types and therapies, showing improved performance over existing methods.

The paper investigates the co-occurrence of chromosome 10 loss and chromosome 7 gain in gliomas, finding functional rescue interactions on chromosome 7 that may compensate for chromosome 10 loss.

The paper develops computational methods combining sequencing and miFISH data to improve accurate inference of clonal copy number evolution in tumors.

The paper develops CODEFACS and LIRICS to deconvolve bulk gene expression and identify clinically relevant ligand-receptor interactions in tumors, finding specific T-cell interactions linked to anti-PD1 response.

PhISCS-BnB computes the most likely perfect tumor phylogeny from single-cell sequencing data, offering optimality guarantees and significant speed improvements over existing methods.

The paper develops methods to deconvolve tumor copy number data by integrating bulk and single-cell sequencing data, improving accuracy over bulk data alone.