Robustness analysis of the entropic regularization parameter in the Gromov–Wasserstein alignment step of TransMarker.

Robustness analysis of the entropic regularization parameter in the Gromov–Wasserstein alignment step of TransMarker.

<p>(A) Classification performance (ACC, AUROC, and AUPRC) across . (B) Spearman rank correlation of gene prioritizations relative to the default . (C) Mean rank deviation of the top 20 biomarkers across <i>ε</i> values.</p> <p>(EPS)</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Fatemeh Keikha (20917848) (author)
مؤلفون آخرون:	Chuanyuan Wang (22676721) (author), Zhixia Yang (3180567) (author), Zhi-Ping Liu (149304) (author)
منشور في:	2025
الموضوعات:	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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