Overview of the TransMarker framework for identifying DNBs from single cell time course data.

Overview of the TransMarker framework for identifying DNBs from single cell time course data.

<p>(A) State specific gene expression profiles are integrated with a prior regulatory network to construct attributed gene interaction graphs. (B) A set of rewired graphs is generated to represent disease progression across multiple states. (C) Topological Feature Integration incorporates both...

תיאור מלא

שמור ב:
מידע ביבליוגרפי
מחבר ראשי: Fatemeh Keikha (20917848) (author)
מחברים אחרים: Chuanyuan Wang (22676721) (author), Zhixia Yang (3180567) (author), Zhi-Ping Liu (149304) (author)
יצא לאור: 2025
נושאים:
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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