بدائل البحث:
clinical decrease » clinical disease (توسيع البحث), clinical case (توسيع البحث), linear decrease (توسيع البحث)
fold decrease » fold increase (توسيع البحث), fold increased (توسيع البحث), fold increases (توسيع البحث)
nn decrease » _ decrease (توسيع البحث), a decrease (توسيع البحث), mean decrease (توسيع البحث)
clinical decrease » clinical disease (توسيع البحث), clinical case (توسيع البحث), linear decrease (توسيع البحث)
fold decrease » fold increase (توسيع البحث), fold increased (توسيع البحث), fold increases (توسيع البحث)
nn decrease » _ decrease (توسيع البحث), a decrease (توسيع البحث), mean decrease (توسيع البحث)
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Identification of ACADM, ANGPTL4, and NFKB2 as significant predictors of OS in the TCGA-KIRC cohort.
منشور في 2025الموضوعات: -
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Kaplan-Meier curves and time-dependent Cox regression for outcome of mortality.
منشور في 2024الموضوعات: -
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Cox regression models for adverse cardiovascular events (ACE) and individual components of ACE.
منشور في 2024الموضوعات: -
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Baseline demographics and comorbidities by presence of post-treatment adverse cardiovascular events.
منشور في 2024الموضوعات: -
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PCA-CGAN K-fold experiment table.
منشور في 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.
منشور في 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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