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Related Experiment Video

Updated: Apr 21, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization.

Jeremy L Warner¹, Joshua C Denny², David A Kreda³

¹Division of Hematology/Oncology, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA.

Journal of the American Medical Informatics Association : JAMIA

|October 23, 2014

Summary

This summary is machine-generated.

Network visualization of electronic health records reveals hidden patient subgroups and disease connections. This method uncovers complex phenomic relationships, aiding clinical hypothesis generation and understanding of patient subpopulations.

Keywords:

Data display Data mining Decision making; computer-assisted Medical informatics applications Phenotype

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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Published on: August 21, 2019

Area of Science:

Biomedical Informatics
Computational Biology
Data Visualization

Background:

Electronic health records (EHRs) contain vast amounts of data.
Identifying complex phenomic relationships within EHRs is challenging.
Traditional analysis methods may miss subtle disease patterns.

Purpose of the Study:

To uncover unrecognized phenomic relationships using network visualization of EHR data.
To develop and demonstrate a software concept, the Phenomics Advisor, for interactive phenotype visualization.
To explore emergent patient clusters and disease associations.

Main Methods:

Utilized force-based network visualization on EHR data from the Multiparameter Intelligent Monitoring in Intensive Care II database.
Defined primary phenotypes from patient profiles.
Compared network visualizations of primary relationships with those incorporating secondary adjacencies.
Applied the Phenomics Advisor software concept for interactive exploration.

Main Results:

Subendocardial infarction with cardiac arrest was used as a sample phenotype, revealing 5423 relationships.
Initial visualization highlighted treatment complications and rare diagnoses.
Incorporating secondary relationships identified an emergent cluster of smokers with metabolic syndrome.
Network visualization revealed phenotypic patterns not apparent through pairwise correlation.

Conclusions:

Network visualization is effective in uncovering complex and previously unrecognized phenomic relationships.
Interactive visualization tools like the Phenomics Advisor can aid clinicians and researchers in hypothesis generation.
This approach offers potential for point-of-care tools to enhance understanding of patient subpopulations.