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Shawn Gomez

Peter Tong Department Chair and Professor

University of Wisconsin-Madison

United States

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About

Shawn Gomez is a Professor at the University of North Carolina at Chapel Hill, USA. His research focuses on the kinome landscape of pancreatic cancer, the predictive capabilities of kinome state in cancer studies, and the application of machine learning to predict clinical outcomes in gastrointestinal diseases. Recent articles emphasize the integration of transcriptomic analysis and artificial intelligence to improve therapeutic approaches and patient management in various cancer types.

Articles (21)

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UNITED STATES

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Elisabeth Tracy

Director of Pediatric Surgical Oncology

University of North Carolina at Chapel Hill

UNITED STATES

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Lilei Zhang

Associate Professor

Baylor College of Medicine

UNITED STATES

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Shawn Gomez

Research Interests

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Articles (21)

The MEK inhibitor trametinib incurs mitochondrial injury and induces innate immune responses in the mouse heart

Artificial intelligence in rare pediatric solid tumor research and clinical care: A scoping review

Kinome profiling allows examination and prediction of kinase inhibitor cardiotoxicity.

DeCAF defines clinical fibroblast subtypes and multidimensional tumor-stroma crosstalk shaping prognosis and immunotherapy response.

Photodynamic therapy simultaneously induces ferroptosis- and apoptosis-like lipid signatures in ovarian cancer cells

Tumor-intrinsic kinome landscape of pancreatic cancer reveals new therapeutic approaches.

Kinome state is predictive of cell viability in pancreatic cancer tumor and cancer-associated fibroblast cell lines.

Post-operative Crohn's Disease Recurrence and Infectious Complications: A Transcriptomic Analysis.

Illuminating function of the understudied druggable kinome

Linking gene expression to clinical outcomes in pediatric Crohn's disease using machine learning.

Predicting readmission after bariatric surgery using machine learning

Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors.

Prediction of Pathologic Complete Response for Rectal Cancer Based on Pretreatment Factors Using Machine Learning.

Analysis of Specimen Mammography with Artificial Intelligence to Predict Margin Status.

Application of machine learning to predict postoperative gastrointestinal bleed in bariatric surgery.

Development and Validation of Machine Learning Models to Predict Readmission After Colorectal Surgery

Differential Performance of Machine Learning Models in Prediction of Procedure-Specific Outcomes

Direct Detection of Glutathione Biosynthesis, Conjugation, Depletion and Recovery in Intact Hepatoma Cells

EMBER: multi-label prediction of kinase-substrate phosphorylation events through deep learning

The Dark Kinase Knowledgebase: an online compendium of knowledge and experimental results of understudied kinases

Stem/Proliferative and Differentiated Cells within Primary Murine Colonic Epithelium Display Distinct Intracellular Free Ca<sup>2+</sup> Signal Codes

Collaborators (12)

Jonathan Schisler

Michael East

Lisa Anne Carey

Marta Overchuk

Timothy Farrell

Huang‐Chiao Huang

Meaghan Kennedy Ng

Brian Jensen

Kelsey Fisher-Wellman

Xianlu Laura Peng

Elisabeth Tracy

Lilei Zhang

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