Table 2_SCPEP1+ basal cells are associated with the remodeling of oxidative stress signaling networks in idiopathic pulmonary fibrosis.xlsx by Xiang Zhou (164107)

“…Oxidative stress scores were calculated using multiple enrichment algorithms, and machine learning models (LASSO, Random Forest, Boruta, Bayesian, LVQ, Treebag) were applied to identify robust OS-related diagnostic biomarkers. …”

Image 1_SCPEP1+ basal cells are associated with the remodeling of oxidative stress signaling networks in idiopathic pulmonary fibrosis.jpeg by Xiang Zhou (164107)

“…Oxidative stress scores were calculated using multiple enrichment algorithms, and machine learning models (LASSO, Random Forest, Boruta, Bayesian, LVQ, Treebag) were applied to identify robust OS-related diagnostic biomarkers. …”

Table 3_SCPEP1+ basal cells are associated with the remodeling of oxidative stress signaling networks in idiopathic pulmonary fibrosis.xlsx by Xiang Zhou (164107)

“…Oxidative stress scores were calculated using multiple enrichment algorithms, and machine learning models (LASSO, Random Forest, Boruta, Bayesian, LVQ, Treebag) were applied to identify robust OS-related diagnostic biomarkers. …”

Table 10_Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine le... by Changjin Wei (21751352)

Table 6_Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine lea... by Changjin Wei (21751352)

Table 8_Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine lea... by Changjin Wei (21751352)

Table 2_Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine lea... by Changjin Wei (21751352)

Table 3_Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine lea... by Changjin Wei (21751352)

Table 7_Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine lea... by Changjin Wei (21751352)

Table 11_Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine le... by Changjin Wei (21751352)

Table 9_Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine lea... by Changjin Wei (21751352)

Table 4_Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine lea... by Changjin Wei (21751352)

Table 5_Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine lea... by Changjin Wei (21751352)

Table 1_Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine lea... by Changjin Wei (21751352)

Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification by Vaibhavi Patel (19843282)

“…<p>Imbalanced class distributions pose a prevalent challenge in numerous classification problems, requiring effective strategies for learning from such skewed data. Traditional machine learning algorithms often struggle with imbalanced datasets, as they tend to bias their classification functions toward the majority class, resulting in suboptimal performance for minority classes. …”

Table 3_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.xlsx by Jian Wang (5901)

“…Subsequent analysis of subtype feature genes was conducted using the weighted gene co-expression network analysis (WGCNA). Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …”

Table 1_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.xlsx by Jian Wang (5901)

“…Subsequent analysis of subtype feature genes was conducted using the weighted gene co-expression network analysis (WGCNA). Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …”

Image 1_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.tif by Jian Wang (5901)

“…Subsequent analysis of subtype feature genes was conducted using the weighted gene co-expression network analysis (WGCNA). Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …”

Table 2_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.xlsx by Jian Wang (5901)

“…Subsequent analysis of subtype feature genes was conducted using the weighted gene co-expression network analysis (WGCNA). Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …”

Detailed sequences of primers for PCR. by Jiangli Zhao (2308330)

“…Our findings also suggested that active m7G levels could promote oxidative phosphorylation in the neurons of epilepsy patients and decrease neuronal necroptosis activity. Machine learning algorithms were used to identify key m7G regulators (EIF4E3, NUDT3, SNUPN, LSM1, and METTL1), and a nomogram model was constructed based on these findings. …”