Table 2_OFGPMA: Optimal frequency graph representation learning for pseudogene and miRNA association prediction.docx

Table 2_OFGPMA: Optimal frequency graph representation learning for pseudogene and miRNA association prediction.docx

<p>Pseudogenes are genomic segments that resemble functional genes structurally yet remain biologically inactive. MicroRNAs (miRNAs), a subclass of non-coding RNAs, are critical regulators of various cellular mechanisms. These pseudogenes and miRNAs interact mutually, forming competitive endog...

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Bibliografische gegevens
Hoofdauteur:	Yongbin Zeng (529358) (author)
Andere auteurs:	Lixiang Xiong (20280585) (author), Yungui Luo (22684343) (author)
Gepubliceerd in:	2025
Onderwerpen:	Genetics optimal frequency graph global random walk with restart local enclosing subgraph graph representation learning pseudogene and miRNA association prediction
Tags:	Voeg label toe Geen labels, Wees de eerste die dit record labelt!

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Table 1_OFGPMA: Optimal frequency graph representation learning for pseudogene and miRNA association prediction.xlsx
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Data Sheet 1_MicroRNA identification and expression analysis of wheat thermo-sensitive male sterile line BNS366 for fertility transformation.zip
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Diversity of augmented graphs.
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Structural and node-level augmentations for graph anomalies.
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Condensed summary of graph anomaly detection methods and their key strengths and limitations.
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Schematic representation of key findings.
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Comparison of anthropometric measurements, body composition and clinical variables among participants with low-grade MI and SLE and according to BMI status of the population.
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Ablation on Amazon.
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Anomaly detection performance (AUROC).
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Abbreviations and notation.
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Normalized total loss on the Facebook dataset (DCOR with and without RLC).
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Normalized training loss vs. baselines (Facebook).
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Dual autoencoder with reconstruction-level contrast.
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Embedding vs. reconstruction-level contrast (RLC).
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Anomalies in attributed networks.
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Quantitative performance comparison of our method and five multilayer network ranking baselines on the GAC dataset across multiple evaluation metrics.
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Classification performance across the three simulation settings.
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Comparison of Gene Discovery and Performance Evaluation with Baseline Methods.
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Case study on Esophageal Squamous Cell Carcinoma (ESCC).
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Ablation studies and performance evaluation under varying training conditions.
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classification results on simulated datasets and biomarkers and classification results for GAC dataset.
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Simulation networks and gene expression analysis across disease states.
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Overview of the TransMarker framework for identifying DNBs from single cell time course data.
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Robustness analysis of the entropic regularization parameter in the Gromov–Wasserstein alignment step of TransMarker.
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Robustness of TransMarker concerning the selection and completeness of the prior knowledge network.
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UpSet diagram illustrating the overlap of DNBs selected by our method and top-ranked genes by the 13 baseline centrality-based ranking algorithms.
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Functional enrichment analysis of the 28 DNB genes identified for ESCC.
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Functional enrichment analysis of the DNBs identified for GAC.
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Mean expression levels of biomarker genes across various disease states.
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Gene regulatory networks for different states of GAC post-rewiring.
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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.
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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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Patterns of connectivity were different depending on time alignment.
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Patterns of mnemonic differences in phase clustering are dependent on time alignment.
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High-frequency broadband (HFB) peak latency timing changes with memory.
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Task and channel coverage.
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