Figure S4. Receiver operating characteristic curves for the prediction of SGA among multiparous women (26Â weeks) using elastic net, classification trees, random forest, gradient boosting, and neural networks. (PDF 189 kb
Table S3. Predicted breast cancer driver genes by the seven permutation models. Table S4. Predicted ...
Tables S1âS8. Supplementary methods. Details of measurement of socioeconomic, family and perinatal...
Supplementary methods including model specification of fetal growth trajectory analyses and details ...
Figure S2. Receiver operating characteristic curves for the prediction of SGA among primiparous wome...
Figure S1. Receiver operating characteristic curves for the prediction of SGA among primiparous wome...
Figure S6. Receiver operating characteristic curves for the prediction of LGA among primiparous wome...
Figure S8. Receiver operating characteristic curves for the prediction of LGA among multiparous wome...
Figure S7. Receiver operating characteristic curves for the prediction of LGA among multiparous wome...
Table S3. Area under the curve in the training data for logistic regression and five machine learnin...
Table S1. Predictors of fetal growth abnormalities and their use in the prediction models. (PDF 71 k...
Table S2. Training parameter grids and parameters used for five machine learning methods for the pre...
Figure showing the structure of the original Terneuzen Birth Cohort data, the broken stick data, and...
Associations between fetal sex and weightâ repeated measurements analyses. (PDF 86 kb
Additional file 1: Table S1. Basic characteristics of the cohort data. Table S2. The RMSE (g) of dif...
Child Health Surveillance Programme- Pre-school review coverage. Table S2. Descriptive statistics of...
Table S3. Predicted breast cancer driver genes by the seven permutation models. Table S4. Predicted ...
Tables S1âS8. Supplementary methods. Details of measurement of socioeconomic, family and perinatal...
Supplementary methods including model specification of fetal growth trajectory analyses and details ...
Figure S2. Receiver operating characteristic curves for the prediction of SGA among primiparous wome...
Figure S1. Receiver operating characteristic curves for the prediction of SGA among primiparous wome...
Figure S6. Receiver operating characteristic curves for the prediction of LGA among primiparous wome...
Figure S8. Receiver operating characteristic curves for the prediction of LGA among multiparous wome...
Figure S7. Receiver operating characteristic curves for the prediction of LGA among multiparous wome...
Table S3. Area under the curve in the training data for logistic regression and five machine learnin...
Table S1. Predictors of fetal growth abnormalities and their use in the prediction models. (PDF 71 k...
Table S2. Training parameter grids and parameters used for five machine learning methods for the pre...
Figure showing the structure of the original Terneuzen Birth Cohort data, the broken stick data, and...
Associations between fetal sex and weightâ repeated measurements analyses. (PDF 86 kb
Additional file 1: Table S1. Basic characteristics of the cohort data. Table S2. The RMSE (g) of dif...
Child Health Surveillance Programme- Pre-school review coverage. Table S2. Descriptive statistics of...
Table S3. Predicted breast cancer driver genes by the seven permutation models. Table S4. Predicted ...
Tables S1âS8. Supplementary methods. Details of measurement of socioeconomic, family and perinatal...
Supplementary methods including model specification of fetal growth trajectory analyses and details ...