Matthew E. Berginski

The paper links kinase inhibitor treatment effects to cell line responses using integrated kinome profiling data and builds predictive models with high accuracy.

The paper predicts cancer cell line sensitivity to kinase inhibitor combinations using kinome inhibition states and machine learning, achieving high accuracy in both simulated and experimental settings.

The paper predicts cell viability in cancer cell lines using kinase inhibitor profiles and gene expression data, achieving R2 of 0.78 and RMSE of 0.154.

The paper links kinome state with cell viability in PDAC tumor and stroma cell lines using regression and classification models, finding predictive relationships and key kinases driving viability responses.

This paper refines the definition of the human kinome, identifies understudied kinases, and highlights their potential as drug targets through differential expression, essentiality, and mutation data.

Coral provides an interactive web tool for visualizing human kinome data with customizable features and high-resolution outputs.