Comparison of Gene Discovery and Performance Evaluation with Baseline Methods.

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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Detalles Bibliográficos
Autor principal: Fatemeh Keikha (20917848) (author)
Otros Autores: Chuanyuan Wang (22676721) (author), Zhixia Yang (3180567) (author), Zhi-Ping Liu (149304) (author)
Publicado: 2025
Materias:
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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Descripción
Sumario:<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>

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