Search alternatives:
clinical decrease » clinical disease (Expand Search), clinical case (Expand Search), linear decrease (Expand Search)
significant fold » significant force (Expand Search), significant co (Expand Search), significant all (Expand Search)
fold decrease » fold increase (Expand Search), fold increased (Expand Search), fold increases (Expand Search)
clinical decrease » clinical disease (Expand Search), clinical case (Expand Search), linear decrease (Expand Search)
significant fold » significant force (Expand Search), significant co (Expand Search), significant all (Expand Search)
fold decrease » fold increase (Expand Search), fold increased (Expand Search), fold increases (Expand Search)
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Identification of ACADM, ANGPTL4, and NFKB2 as significant predictors of OS in the TCGA-KIRC cohort.
Published 2025Subjects: -
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Kaplan-Meier curves and time-dependent Cox regression for outcome of mortality.
Published 2024Subjects: -
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Cox regression models for adverse cardiovascular events (ACE) and individual components of ACE.
Published 2024Subjects: -
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Baseline demographics and comorbidities by presence of post-treatment adverse cardiovascular events.
Published 2024Subjects: -
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PCA-CGAN K-fold experiment table.
Published 2025“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
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PCAECG-GAN K-fold experiment table.
Published 2025“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
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