ARL values for the AIB-GWMA-ME charts with linearly increasing variance method by taking , , , when . حسب Jen-Hsiang Chen (22339551)

"…<p>ARL values for the AIB-GWMA-ME charts with linearly increasing variance method by taking , , , when .…"

ARL values for the AIB-GWMA-ME charts with linearly increasing variance method by taking , , , when . حسب Jen-Hsiang Chen (22339551)

"…<p>ARL values for the AIB-GWMA-ME charts with linearly increasing variance method by taking , , , when .…"

Loss of ECRG4 expression decreases neutrophil mobilization from bone marrow reserves. حسب Katie D. Pool (20141584)

الموضوعات:

Biases in larger populations. حسب Sander W. Keemink (21253563)

"…<p>(<b>A</b>) Maximum absolute bias vs the number of neurons in the population for the Bayesian decoder. …"

The distribution of <i>Giardia duodenalis</i> assemblages in domestic and wild populations of nonhuman mammalian species with a larger number of isolates. حسب Kareem Hatam-Nahavandi (7384790)

<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" حسب Kira Shaw (18796168)

The introduction of mutualisms into assembled communities increases their connectance and complexity while decreasing their richness. حسب Gui Araujo (22170819)

"…When they stop being introduced in further assembly events (i.e. introduced species do not carry any mutualistic interactions), their proportion slowly decreases with successive invasions. (B) Even though higher proportions of mutualism promote higher richness, introducing this type of interaction into already assembled large communities promotes a sudden drop in richness, while stopping mutualism promotes a slight boost in richness increase. …"

<b>Nest mass in forest tits </b><b><i>Paridae</i></b><b> </b><b>increases with elevation and decreasing body mass, promoting reproductive success</b> حسب Clara Wild (19246606)

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). …"

Domain organization of human αB-crystallin protein. حسب Aref Baharvand (21713886)

Chaperone activity and cytoprotective effects of the αB-crystallin proteins. حسب Aref Baharvand (21713886)