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Related Experiment Video

Updated: Apr 5, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Modeling Protein Expression and Protein Signaling Pathways.

Donatello Telesca¹, Peter Müller², Steven M Kornblau³

¹Assistant Professor, Department of Biostatistics, UCLA School of Public Health, Los Angeles, CA.

Journal of the American Statistical Association

|August 7, 2015

Summary

This study introduces a new probability model to understand protein interactions and pathway activation using hidden variables. The model helps identify key molecular pathways in diseases like acute myeloid leukemia.

Keywords:

AML Graphical models Mixture models POE RJ-MCMC RPPA

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Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Published on: August 17, 2015

Area of Science:

Proteomics
Systems Biology
Statistical Modeling

Background:

High-throughput functional proteomic technologies enable protein expression quantification.
Statistical inference is crucial for identifying protein activation states and molecular interaction patterns.
Understanding protein dependence structure reveals properties of molecular pathways.

Purpose of the Study:

To propose a novel probability model for representing molecular interactions using hidden binary latent variables.
To leverage expert knowledge for an informative prior on hidden conditional dependence structure.
To facilitate model determination and identify relevant pathway interactions.

Main Methods:

Developed a probability model with hidden binary latent variables representing protein activity states.
Incorporated expert knowledge to define an informative prior on the hidden conditional dependence structure.
Applied the model to reverse-phase protein array data from acute myeloid leukemia studies.

Main Results:

The model effectively represents molecular interactions and protein activation states.
The informative prior guided the model space towards relevant interactions.
Inference successfully identified significant subpathways in acute myeloid leukemia.

Conclusions:

The proposed probability model offers a robust framework for analyzing protein interaction networks.
Integrating expert knowledge enhances the interpretability and efficiency of pathway analysis.
This approach aids in uncovering disease-specific molecular mechanisms and potential therapeutic targets.