Robustness of TransMarker concerning the selection and completeness of the prior knowledge network.

<p>Performance comparison of TransMarker when utilizing different prior interaction networks (HumanNet, InBioMap, STRINGdb) and under progressive random edge removal (–5%, –10%, –15%, and –20%) from the original RegNetwork prior. Bars represent across multiple runs for ACC, AUROC, and AUPRC.&l...

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Bibliographic Details
Main Author:	Fatemeh Keikha (20917848) (author)
Other Authors:	Chuanyuan Wang (22676721) (author), Zhixia Yang (3180567) (author), Zhi-Ping Liu (149304) (author)
Published:	2025
Subjects:	Medicine Genetics Biotechnology Cancer Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified wasserstein optimal transport real world dataset quantified via gromov enhancing diagnostic precision dynamic network index deep neural network ablation studies confirm regulatory role transitions gene regulatory networks combining regulatory rewiring analyzing disease evolution temporal expression dynamics reflect dynamic changes neglecting structural rewiring multilayer network models modeling disease progression state alignment provides understanding cancer progression state graph alignment disease state classification state specific biomarkers disease progression state alignment specific expression multilayer graph cancer progression disease state regulatory variability regulatory roles structural shifts significant changes prioritized biomarkers classification accuracy state single xlink "> topological features synthetic simulated ranked using powerful strategy overall performance meaningful shifts gats ), gastric adenocarcinoma gac ), framework designed findings suggest dni ), distinct layer contextualized embeddings cell data
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Robustness of TransMarker concerning the selection and completeness of the prior knowledge network.

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