Eric A. Sobie

The paper introduces predicTox, a database integrating clinical risk scores and gene expression data to identify genomic variants and pathways linked to drug-induced cardiotoxicity.

The paper shows that transient Notch activation converts human pluripotent stem cell-derived cardiomyocytes into Purkinje fiber-like cells with altered electrophysiology and gene expression.

The paper identifies cardiotoxic drug signatures using bulk transcriptomics and integrates them with genomic and single-cell data to predict toxicity mechanisms.

This study identifies Plexin-B2 as a regulator of cell mechanics during multicellular organization, showing its role in modulating cell stiffness, contractility, and differentiation in human stem and progenitor cells.

This paper introduces structural gene expression signatures (sGES) derived from protein structure features, showing their utility in identifying robust biological signatures across datasets and experimental platforms.

The paper constructs a tissue- and organ-based subcellular-function atlas of obesity-related genes and pathways using publicly available datasets and integration methods.

The paper introduces Bwb, a tool for creating and executing reproducible bioinformatics workflows via a drag-and-drop interface using Docker containers.

The paper optimizes an RNA-seq workflow for multi-threaded environments, achieving significant speed improvements by optimizing all steps, not just alignment.

The paper develops a cross-cell type regression model to predict drug responses in adult cardiac myocytes using iPSC-CM data, showing accurate predictions for both healthy and diseased cells.

This study compares 3-DGE and conventional mRNAseq, finding similar gene expression results with 3-DGE offering cost savings and scalability.

The paper identifies fragile physiological dynamics of ion channel kinetics associated with genomic determinants of arrhythmias and validates this relationship using RNA sequencing.

This paper demonstrates that RNA-seq analysis can depend on the computing environment and shows that software containers improve reproducibility.