Latent state estimations for experimental data with no stressors for subject 13. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 21. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 6. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 8. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 12. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 22. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 1. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 4. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 10. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 10. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 2. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 12. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 20. حسب Samiul Alam (18563323)

Triplet Matching for Estimating Causal Effects With Three Treatment Arms: A Comparative Study of Mortality by Trauma Center Level حسب Giovanni Nattino (561797)

"…Propensity score matching is a popular method to infer causal relationships in observational studies with two treatment arms. Few studies, however, have used matching designs with more than two groups, due to the complexity of matching algorithms. …"

DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx حسب Yuhong Huang (115702)

"…We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). …"

Latent state estimations for experimental data with time limit for subject 17. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 13. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 16. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 20. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 5. حسب Samiul Alam (18563323)