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...

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Номзүйн дэлгэрэнгүй
Үндсэн зохиолч:	Fatemeh Keikha (20917848) (author)
Бусад зохиолчид:	Chuanyuan Wang (22676721) (author), Zhixia Yang (3180567) (author), Zhi-Ping Liu (149304) (author)
Хэвлэсэн:	2025
Нөхцлүүд:	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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Ижил төстэй зүйлс

Robustness of TransMarker concerning the selection and completeness of the prior knowledge network.
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

Robustness analysis of the entropic regularization parameter in the Gromov–Wasserstein alignment step of TransMarker.
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

Functional enrichment analysis of the DNBs identified for GAC.
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

UpSet diagram illustrating the overlap of DNBs selected by our method and top-ranked genes by the 13 baseline centrality-based ranking algorithms.
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

Quantitative performance comparison of our method and five multilayer network ranking baselines on the GAC dataset across multiple evaluation metrics.
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

Classification performance across the three simulation settings.
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

Comparison of Gene Discovery and Performance Evaluation with Baseline Methods.
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

Case study on Esophageal Squamous Cell Carcinoma (ESCC).
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

Ablation studies and performance evaluation under varying training conditions.
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

classification results on simulated datasets and biomarkers and classification results for GAC dataset.
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

Simulation networks and gene expression analysis across disease states.
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

Functional enrichment analysis of the 28 DNB genes identified for ESCC.
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

Mean expression levels of biomarker genes across various disease states.
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

Gene regulatory networks for different states of GAC post-rewiring.
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

UMAP visualizations of extracted CD8<sup> + </sup> T and CD4<sup> + </sup> T cell barcodes, employed for downstream subsequent single-cell trajectory and classification analyses.
-н: Fatemeh Keikha (20917848)
Хэвлэсэн: (2025)

Schematic representation of key findings.
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Хэвлэсэн: (2025)

Individual memory performance was predicted by a subset of iEEG signal components.
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Short periods of increased connectivity between ROIs are characterized by a sparse overall network state.
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Хэвлэсэн: (2025)

Patterns of connectivity were different depending on time alignment.
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Хэвлэсэн: (2025)

Patterns of mnemonic differences in phase clustering are dependent on time alignment.
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Хэвлэсэн: (2025)

High-frequency broadband (HFB) peak latency timing changes with memory.
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Хэвлэсэн: (2025)

Task and channel coverage.
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Хэвлэсэн: (2025)

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Data Sheet 1_Insights into Mycobacterium abscessus survival under prolonged potassium deficiency and starvation.pdf
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Romanian start-ups
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Supporting Information
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OH Training Database and ACE Potential
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Presentation 1_Building regulatory resilience in shipping: green technology licensing and carbon tax policies.pdf
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Data Sheet 1_Building regulatory resilience in shipping: green technology licensing and carbon tax policies.zip
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Supplementary file 1_Mechanisms of anti-nutritional factor degradation and quality enhancement in soybean meal via Rpf-mediated microbiota synergy and Bacillus reinforcement during solid-state fermentation.docx
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