Validation of FGF9 expression in external database and NRK-52E cells. by Donglin Yang (490406)

“…(C) AUC value of FGF9 in the GSE30122 database. (D) The AUC value of FGF9 in the GSE96804 database. …”

Table 2_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…The Area Under the Curve (AUC) for the training sets generally ranged from 0.75 to 0.99, which decreased to 0.70 to 0.87 during internal validation. …”

Table 4_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…The Area Under the Curve (AUC) for the training sets generally ranged from 0.75 to 0.99, which decreased to 0.70 to 0.87 during internal validation. …”

Image 2_Prognostic model construction and immune microenvironment analysis of pyroptosis-related genes in hepatocellular carcinoma based on single-cell RNA sequencing.tiff by Haoming Shen (3816988)

“…The prognostic model developed in this study stratified patients into high-risk and low-risk categories based on eight significant genes, achieving area under the curve (AUC) values of 0.73, 0.65, and 0.69 for overall survival at one-year, two-year, and three-year intervals, respectively. …”

Image 1_Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.tif by Haipeng Zhang (3413288)

“…A diagnostic model based on these genes yielded area under the curve (AUC) values of 0.880 in the training dataset and 0.936 in the validation dataset. …”

Table 3_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…The Area Under the Curve (AUC) for the training sets generally ranged from 0.75 to 0.99, which decreased to 0.70 to 0.87 during internal validation. …”

Table 1_Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.docx by Haipeng Zhang (3413288)

“…A diagnostic model based on these genes yielded area under the curve (AUC) values of 0.880 in the training dataset and 0.936 in the validation dataset. …”

Table 1_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…The Area Under the Curve (AUC) for the training sets generally ranged from 0.75 to 0.99, which decreased to 0.70 to 0.87 during internal validation. …”

Image 1_Prognostic model construction and immune microenvironment analysis of pyroptosis-related genes in hepatocellular carcinoma based on single-cell RNA sequencing.tiff by Haoming Shen (3816988)

“…The prognostic model developed in this study stratified patients into high-risk and low-risk categories based on eight significant genes, achieving area under the curve (AUC) values of 0.73, 0.65, and 0.69 for overall survival at one-year, two-year, and three-year intervals, respectively. …”

Data Sheet 1_Prognostic model construction and immune microenvironment analysis of pyroptosis-related genes in hepatocellular carcinoma based on single-cell RNA sequencing.zip by Haoming Shen (3816988)

“…The prognostic model developed in this study stratified patients into high-risk and low-risk categories based on eight significant genes, achieving area under the curve (AUC) values of 0.73, 0.65, and 0.69 for overall survival at one-year, two-year, and three-year intervals, respectively. …”

Data Sheet 2_Metabolomics reveals key biomarkers for ischemic stroke: a systematic review of emerging evidence.pdf by Lu Ding (475637)

“…Metabolite analysis revealed associations between decreased proline, isoleucine, valine, and alanine levels with IS. …”

Data Sheet 1_Metabolomics reveals key biomarkers for ischemic stroke: a systematic review of emerging evidence.pdf by Lu Ding (475637)

“…Metabolite analysis revealed associations between decreased proline, isoleucine, valine, and alanine levels with IS. …”

Data Sheet 3_Metabolomics reveals key biomarkers for ischemic stroke: a systematic review of emerging evidence.pdf by Lu Ding (475637)

“…Metabolite analysis revealed associations between decreased proline, isoleucine, valine, and alanine levels with IS. …”

Data Sheet 4_Metabolomics reveals key biomarkers for ischemic stroke: a systematic review of emerging evidence.pdf by Lu Ding (475637)

“…Metabolite analysis revealed associations between decreased proline, isoleucine, valine, and alanine levels with IS. …”

Data Sheet 1_Increased circulating levels of SP-D and IL-10 are associated with the development of disease severity and pulmonary fibrosis in patients with COVID-19.pdf by Xin Pan (3712768)

“…Among the biomarkers indicative of macrophage polarization, compared to non-ARDS patients, a significant increase in IL-10, Inducible nitric oxide synthase (iNOS), and Arginase-1 (Arg-1) were observed in ARDS patients, while Tumor necrosis factor-α (TNF-α) was decreased. The plasma level of IL-10 was also elevated in patients with fibrotic changes on CT, and was positively correlated with ACE2 and Arg-1. …”

Data Sheet 1_Application of machine learning based on habitat imaging and vision transformer to predict treatment response of locally advanced esophageal squamous cell carcinoma fo... by Shu-Han Xie (17902661)

“…Similarly, ExtraTrees showed good predictive capabilities in patients undergoing 2 cycles of nICT with AUC of 0.862 in validation cohort. This model also showed good calibration for prediction probability and satisfied clinical value on DCAs. …”

Image 2_Establishment and validation of survival nomogram score staging for esophageal squamous cell carcinoma patients after minimally invasive surgery combined with immune progno... by Shao-jun Xu (11633956)

“…To evaluate the predictive value and the clinical benefit rate of the nomogram, we employed the calibration curve, decision curve analysis (DCA), and time-dependent area under the curve (t-AUC). …”

Image 1_Establishment and validation of survival nomogram score staging for esophageal squamous cell carcinoma patients after minimally invasive surgery combined with immune progno... by Shao-jun Xu (11633956)

“…To evaluate the predictive value and the clinical benefit rate of the nomogram, we employed the calibration curve, decision curve analysis (DCA), and time-dependent area under the curve (t-AUC). …”

Image 4_Establishment and validation of survival nomogram score staging for esophageal squamous cell carcinoma patients after minimally invasive surgery combined with immune progno... by Shao-jun Xu (11633956)

“…To evaluate the predictive value and the clinical benefit rate of the nomogram, we employed the calibration curve, decision curve analysis (DCA), and time-dependent area under the curve (t-AUC). …”

Image 6_Establishment and validation of survival nomogram score staging for esophageal squamous cell carcinoma patients after minimally invasive surgery combined with immune progno... by Shao-jun Xu (11633956)

“…To evaluate the predictive value and the clinical benefit rate of the nomogram, we employed the calibration curve, decision curve analysis (DCA), and time-dependent area under the curve (t-AUC). …”