sPGGM: a sample-perturbed Gaussian graphical model for identifying pre-disease stages and signaling molecules of disease progression
Peer-Reviewed Publication
Updates every hour. Last Updated: 21-Dec-2025 06:11 ET (21-Dec-2025 11:11 GMT/UTC)
In a paper published in National Science Review, the team of Pro. Liu present an innovative computational framework, the sample-perturbed Gaussian graphical model (sPGGM), designed to analyse disease progression and identify pre-disease stages at the specific sample/cell level based on optimal transport theory and Gaussian graphical models. The proposed sPGGM provides a new single-sample way to identify the pre-disease state and discover signaling molecules leading to potential disease, which showcases exceptional effectiveness and robustness for both bulk and single-cell data analyses, offering a novel perspective for personalized disease prediction.
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