sPGGM: a sample-perturbed Gaussian graphical model for identifying pre-disease stages and signaling molecules of disease progression
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel computational framework, the sample-perturbed Gaussian graphical model (sPGGM), to detect critical pre-disease stages and identify signaling molecules. The method accurately pinpoints disease transitions using complex, high-dimensional biological data.
Area Of Science
- Computational Biology
- Systems Biology
- Biostatistics
Background
- Complex diseases exhibit non-linear progression with critical transition points.
- Identifying these critical states and associated biomarkers is crucial for early intervention.
- Traditional methods struggle with high-dimensional, limited sample, or single-cell data.
Purpose Of The Study
- To develop an innovative computational framework for analyzing disease progression.
- To identify pre-disease stages and dynamic network biomarkers at the sample/cell level.
- To overcome limitations of traditional statistical approaches in complex biological data.
Main Methods
- Developed the sample-perturbed Gaussian graphical model (sPGGM) integrating optimal transport theory and Gaussian graphical models.
- Characterized dynamic differences between baseline and perturbed distributions at the individual sample/cell level.
- Validated the framework using simulated, single-cell, influenza, and bulk tumor datasets.
Main Results
- The sPGGM framework effectively identifies pre-disease stages and signaling molecules.
- Demonstrated improved capability in pinpointing critical points compared to existing single-sample methods.
- Validated reliability and effectiveness across diverse biological datasets, including single-cell and tumor data.
Conclusions
- The sPGGM provides a robust method for detecting critical disease transitions and biomarkers.
- This approach enhances understanding of disease progression dynamics.
- Enables potential for earlier disease intervention and improved patient outcomes.
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