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values decrease » values increased (Expand Search), largest decrease (Expand Search)
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_ part » _ parp (Expand Search)
values decrease » values increased (Expand Search), largest decrease (Expand Search)
part decrease » point decrease (Expand Search), bfrt decreased (Expand Search), a decrease (Expand Search)
_ part » _ parp (Expand Search)
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1
Demographic and ocular features.
Published 2025“…The XGBoost or KNN model using TAS alone achieved the highest AUC (0.74) in five-fold cross-validation.</p><p>Conclusion</p><p>The decrease in TAS levels and the increase in H<sub>2</sub>O<sub>2</sub> and MDA levels are found to be correlated with PCG, and the results indicate that oxidative stress plays a part in congenital glaucoma onset.…”
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2
Machine learning model to diagnose PCG.
Published 2025“…The XGBoost or KNN model using TAS alone achieved the highest AUC (0.74) in five-fold cross-validation.</p><p>Conclusion</p><p>The decrease in TAS levels and the increase in H<sub>2</sub>O<sub>2</sub> and MDA levels are found to be correlated with PCG, and the results indicate that oxidative stress plays a part in congenital glaucoma onset.…”
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3
ROC curves of TAS + SOD + MDA to diagnose PCG.
Published 2025“…The XGBoost or KNN model using TAS alone achieved the highest AUC (0.74) in five-fold cross-validation.</p><p>Conclusion</p><p>The decrease in TAS levels and the increase in H<sub>2</sub>O<sub>2</sub> and MDA levels are found to be correlated with PCG, and the results indicate that oxidative stress plays a part in congenital glaucoma onset.…”
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4
Characteristics of women at admission.
Published 2025“…The PIERS-ML model’s AUC-PRC peaked on day 0 (0.65), and notably decreased thereafter. …”
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5
Development of a machine learning method for predicting neutrophil-specific functional genes.
Published 2025“…<p>(A) NeuRGI model training workflow involved: 1) extracting gene features from various databases. 2) using genes of neutrophil-related genes as positives and PU-learning as negatives. 3) balancing the training set with under-sampling and training the NeuRGI random forest model with 10-fold cross-validation, then employing a Gaussian Mixture Model (GMM) with NeuRGI scores to identify potential positives. 4) using OntoVAE for <i>in silico</i> knockout of GMM-classified genes to find key regulatory factors for guiding follow-up experiments. (B) AUC and PR curves and the mean AUC value for the NeuRGI model trained from 10-fold cross-validation. …”
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6
Data Sheet 1_Altered static and dynamic spontaneous brain activity in patients with dysthyroid optic neuropathy: a resting-state fMRI study.docx
Published 2025“…DON patients also exhibited decreased dALFF in the left LING and right CUN, together with increased dALFF in the right orbital part of the middle frontal gyrus and right SFGdor in comparison to non-DON patients. …”