Comparison of Gene Discovery and Performance Evaluation with Baseline Methods.

<p>(A) Venn diagram comparing our method and five multilayer network baselines, highlighting 19 unique genes identified only by our approach, suggesting its potential to uncover novel GAC-related candidates. (B) Performance comparison between our method and 13 classical centrality-based rankin...

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書目詳細資料
主要作者:	Fatemeh Keikha (20917848) (author)
其他作者:	Chuanyuan Wang (22676721) (author), Zhixia Yang (3180567) (author), Zhi-Ping Liu (149304) (author)
出版:	2025
主題:	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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總結:	<p>(A) Venn diagram comparing our method and five multilayer network baselines, highlighting 19 unique genes identified only by our approach, suggesting its potential to uncover novel GAC-related candidates. (B) Performance comparison between our method and 13 classical centrality-based ranking algorithms on the GAC dataset. (C) Performance comparison between our method and two gene ranking frameworks.</p>

Comparison of Gene Discovery and Performance Evaluation with Baseline Methods.

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