Mean expression levels of biomarker genes across various disease states.

Mean expression levels of biomarker genes across various disease states.

<p>The line plot illustrates the state-wise mean expression of 11 biomarker genes identified as differentially expressed (DEGs) in the study. To improve clarity and minimize visual clutter, only the DEGs among the chosen biomarkers are shown—genes exclusively from the KEGG gastric cancer pathw...

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Bibliografski detalji
Glavni autor: Fatemeh Keikha (20917848) (author)
Daljnji autori: Chuanyuan Wang (22676721) (author), Zhixia Yang (3180567) (author), Zhi-Ping Liu (149304) (author)
Izdano: 2025
Teme:
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
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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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Sažetak:<p>The line plot illustrates the state-wise mean expression of 11 biomarker genes identified as differentially expressed (DEGs) in the study. To improve clarity and minimize visual clutter, only the DEGs among the chosen biomarkers are shown—genes exclusively from the KEGG gastric cancer pathway are excluded from this figure.</p> <p>(EPS)</p>

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