Matthew W. B. Trotter

The paper uses deep learning on images of iPSC-derived motor neurons with fALS mutations to identify disease-relevant phenotypes and molecular differences.

The paper identifies 141 genetic programs in multiple myeloma that predict disease outcomes and reveal mechanisms of relapse and therapy response.

The paper uses network modeling to identify molecular mechanisms driving T cell exhaustion and validates predictions through experimental testing.

The paper identifies disease severity and pharmacodynamic biomarkers in psoriasis and ankylosing spondylitis using plasma proteomic data from apremilast clinical trials.

ADAPTS improves cell type deconvolution in single-cell RNAseq data by customizing signature matrices and using hierarchical clustering to refine cell type identification.

This study compares RNA-seq and junction arrays for detecting splice events using a cross-platform algorithm, finding RNA-seq superior for novel events but both coherent for known regions.