Predictions of weekly newly reported cases of BC for the upcoming 16 weeks since 7 January 2024 using a two-VOCs model learned by the VOCs-INN algorithm. حسب Wenxuan Li (2566450)

Comparison between individual and final models. The GRF and heel model are the models trained using only the ipsilateral GRF signal and only the ipsilateral heel height signal, res... حسب Xiaowen Chen (192197)

الموضوعات:

A. Scatter plot of the training set data before batch correction; B: Scatter plot of the training set data after batch correction. حسب Min-xiao Wang (20868922)

الموضوعات:

Table 5_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls حسب Ming Xie (420493)

"…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …"

Table 8_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls حسب Ming Xie (420493)

"…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …"

Table 7_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls حسب Ming Xie (420493)

"…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …"

Table 4_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls حسب Ming Xie (420493)

"…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …"

Table 6_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls حسب Ming Xie (420493)

"…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …"

Table 3_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls حسب Ming Xie (420493)

"…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …"

Table 2_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls حسب Ming Xie (420493)

"…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …"

Table 1_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls حسب Ming Xie (420493)

"…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …"

Illustrative diagram of the algorithm selection, training dataset building and training/testing process. حسب Leonardo Costa Ribeiro (10439243)

Modeling CO₂ solubility in polyethylene glycol polymer using data driven methods حسب YunLi Lei (21458056)

"…To prevent overfitting, K-fold cross-validation is applied throughout model training. The efficacy of each optimization method is evaluated using computational runtime and performance metrics, including R-squared (R²), mean squared error (MSE), and average absolute relative error (AARE%). …"

Jackknife test of variable importance a) regularized training gain b) test gain c) AUC. حسب Shravani Sanyal (20833595)

الموضوعات:

Comparative Internal and External AUC Performance Across Single-Site Models. حسب Ioana Duta (18462981)

الموضوعات:

Performance of incremental models. حسب Ioana Duta (18462981)

الموضوعات:

Performance of LOSO models. حسب Ioana Duta (18462981)

الموضوعات:

Dataset breakdown. حسب Ioana Duta (18462981)

الموضوعات:

Median and IQR of the model AUC values across each study site for internal and external validation. حسب Ioana Duta (18462981)

الموضوعات:

Variation of model weights between sites. حسب Ioana Duta (18462981)

الموضوعات: