Spatial information of excitatory neurons in APP/PS1 mice are decreased in dCA1 and vCA1. by Udaysankar Chockanathan (18510288)

Odds ratios (ORs) for a green outcome (ie, any green trait going into fixation) with confidence intervals (shaded areas), compared with drift, as cost increases from 0 to 100, for runs where we used different steady values of B. by Jonathan R. Goodman (16715404)

Odds ratios (ORs) for a green outcome (ie, any green trait going into fixation) with confidence intervals (shaded areas), compared with drift, as B increases from 0 to 1, for runs where we used different steady values of cost. by Jonathan R. Goodman (16715404)

Odds ratios for IVW MR estimates for each outcome with <i>cis</i>CRP exposures and are on the scale of lnCRP decrease. by Fergus W. Hamilton (10724401)

Rotation of a CLSM acquisition showing the large decrease in cathepsin D decoration of <i>L</i>.... by Sandrine Cojean (274005)

Terminal objective values for the Gilets Jaunes dataset as a function of <i>s</i>_{<i>network</i>} and <i>s</i>_<i>edge</i>. by Yen-Shao Chen (18264587)

Subjects:

Terminal objective values for the U.S. election dataset as a function of <i>s</i>_{<i>network</i>} and <i>s</i>_<i>edge</i>. by Yen-Shao Chen (18264587)

Subjects:

Precision and recall of ensemble methods and individual tools for enzyme annotation with respect to ECs predictable by any tool and on different sets of proteins. by Nirvana Nursimulu (13762757)

Displays the absolute standard mean differences in sociodemographic characteristics of the sample depending on whether they had engaged in any of the examined aspects of advance ca... by Holly G. Prigerson (8407527)

Locations of study area and sampling sites (Esri reserves the right to grant permission for any other use of the image). by Ziqi Li (7154915)

Pairwise correlations of excitatory neurons in APP/PS1 mice are decreased in dCA1, but increased in vCA1. by Udaysankar Chockanathan (18510288)

Predicted prevalence of any physical limitation at age 50 by survey and period for low versus high SES. by Dana A. Glei (1939324)

Table 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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). …”