Table 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx حسب Hanlin Yu (17776399)

"…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …"

Table 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx حسب Hanlin Yu (17776399)

"…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …"

Table 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx حسب Hanlin Yu (17776399)

"…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …"

Table 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx حسب Hanlin Yu (17776399)

"…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …"

Image 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif حسب Hanlin Yu (17776399)

"…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …"

Image 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif حسب Hanlin Yu (17776399)

"…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …"

Image 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff حسب Hanlin Yu (17776399)

"…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …"

Data Sheet 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.csv حسب Hanlin Yu (17776399)

"…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …"

Image 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff حسب Hanlin Yu (17776399)

"…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …"

Data Sheet 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.csv حسب Hanlin Yu (17776399)

"…Co-expression modules were then identified in the SD and stroke datasets by weighted gene co-expression network analysis (WGCNA), respectively, and machine learning algorithms (RandomForest, LASSO, and XGBoost) were performed to identify ARL2 as a key diagnostic biomarker with high predictive value (AUC = 0.91). …"

Addition of <i>precA</i> decreases the sensitivity of <i>rarA ruvB</i> cells to DNA damage. حسب Kanika Jain (2884439)

Comparison of physiological and perceptual measures of national-level rowing athletes taken during a 10-min submaximal rowing test performed at 95% of 30-min time-trial average pow... حسب Calvin P. Philp (12095878)

Exploring the correlation between the AUC of the ROC curve and ranging the <i>S</i>_<i>f</i> bound from 0.04 to 0.4. حسب Beatriz Ocaña-Tienda (17873813)

In superadditive networks, more enhancers decrease noise and fidelity. حسب Alvaro Fletcher (15675430)

Immunostaining patterns of 5-mC and 5-hmC in normal glandular epithelium and adenocarcinoma in situ (AIS). حسب Jobran M. Moshi (18405149)

Comparison of physiological and perceptual measures taken during Session 2 and Session 10 of a heat acclimation intervention in well-trained rowers. حسب Calvin P. Philp (12095878)

ROC-AUC plot comparison of seven ML models. حسب Xueliang Guo (4797057)

"…By employing skip connections, the model effectively integrates the high-resolution features from the encoder with the up-sampling features from the decoder, thereby increasing the model’s sensitivity to 3D spatial characteristics. …"

Decrease of muscle weakness and degeneration with mutants of UPS components. حسب Cécile Ribot (11944875)

ActAs and CoT prompt strategies do not result in decreased error rate for tasks with increasing complexity. حسب Jacqueline A. Jansen (14894276)

Self-correction prompt strategy results in decreased error rate for all tasks with increasing complexity. حسب Jacqueline A. Jansen (14894276)