Data_Sheet_1_Metagenomic Geolocation Prediction Using an Adaptive Ensemble Classifier.PDF by Samuel Anyaso-Samuel (10671576)

“…Also, we implemented class weighting and an optimal oversampling technique to overcome the class imbalance in the primary data. …”

Supporting data for “<b>Development and Clinical Validation of Novel Biomarkers for Early Diagnosis of Advanced Liver Fibrosis</b>” by Pei Wan (9769043)

DataSheet_1_DNA Damage Response Evaluation Provides Novel Insights for Personalized Immunotherapy in Glioma.pdf by Mu Chen (290262)

Table_4_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

“…On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. …”

Table_2_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

“…On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. …”

Table_1_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.docx by Hui Tang (226667)

“…On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. …”

Table_3_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLS by Hui Tang (226667)

“…On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. …”

Table_5_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

“…On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. …”

Data Sheet 1_Triglyceride-glucose index and mortality in congestive heart failure with diabetes: a machine learning predictive model.doc by Lin Yu (221619)

“…Subgroup analyses were conducted based on age, gender, chronic pulmonary disease, atrial fibrillation, hypertension, and mechanical ventilation to assess the robustness of our findings. Feature selection was performed using LASSO regression, and predictive modeling was carried out using machine learning algorithms.…”

Data Sheet 1_Association between admission Braden Skin Score and delirium in surgical intensive care patients: an analysis of the MIMIC-IV database.docx by Meiling Shang (21086624)

“…The primary outcome was incidence of delirium. Feature importance of BSS was initially assessed using a machine learning algorithm, while restricted cubic spline (RCS) models and multivariable logistic analysis evaluated the relationship between BSS and delirium. …”

Data Sheet 1_Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy.docx by Tobias Schraut (21488108)

Table_1_Prediction of pCR based on clinical-radiomic model in patients with locally advanced ESCC treated with neoadjuvant immunotherapy plus chemoradiotherapy.docx by Xiaohan Wang (691917)

“…Concurrently, related clinical data was amassed. Feature selection was facilitated using the Extreme Gradient Boosting (XGBoost) algorithm, with model validation conducted via fivefold cross-validation. …”

Image 1_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.tif by Dongdong Zhang (644246)

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.…”

Table 2_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.…”

Table 5_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.…”

Table 3_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.…”

Table 1_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.doc by Dongdong Zhang (644246)

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.…”

Table 4_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.…”

Table 6_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.…”