Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale. by Tehnan I. A. Mohamed (16846175)

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

Bayesian variable selection in distributed lag models: a focus on binary quantile and count data regressions by Daniel Dempsey (11805621)

RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values . by Jakub Stoklosa (3315042)

An Extension of the Unified Skew-Normal Family of Distributions and its Application to Bayesian Binary Regression by Paolo Onorati (20461248)

“…We discuss in detail the popular logit case, and we show that, when a logistic regression model is combined with a Gaussian prior, posterior summaries such as cumulants and normalizing constants can easily be obtained through the use of an importance sampling approach, opening the way to straightforward variable selection procedures. …”

Interpretability analysis. by Michela C. Massi (14599915)

Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions by Sung Jae Jun (17309092)

“…<p>We study causal inference under case-control and case-population sampling. Specifically, we focus on the binary-outcome and binary-treatment case, where the parameters of interest are causal relative and attributable risks defined via the potential outcome framework. …”

Table 1_Provider fidelity in tuberculosis screening practices among adolescents and adults living with HIV in public health facilities in Tanzania: a cross-sectional evaluation.doc... by Lucas L. Shilugu (22644302)

“…Simple random and systematic sampling methods were employed to select the records and sessions, respectively. …”

Table 2_Provider fidelity in tuberculosis screening practices among adolescents and adults living with HIV in public health facilities in Tanzania: a cross-sectional evaluation.doc... by Lucas L. Shilugu (22644302)

“…Simple random and systematic sampling methods were employed to select the records and sessions, respectively. …”

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

“…Hence, the study’s primary disadvantage is its small sample size. In this case, the dataset we utilized did not fully reflect the whole population. …”

Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf by Cecilia Lindig-León (7889777)

“…., MIs involving 2 or more body parts at the same time). And we propose two new multilabel uses of the Common Spatial Pattern (CSP) algorithm to optimize the signal-to-noise ratio, namely MC2CMI and MC2SMI approaches. …”

DataSheet_2_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.pdf by Francesca Piludu (10706391)

“…The model with the final feature set was achieved using the support vector machine binary classification algorithm.</p>Results<p>Models for discriminating between Warthin’s and malignant tumors, benign and Warthin’s tumors and benign and malignant tumors had an accuracy of 86.7%, 91.9% and 80.4%, respectively. …”

DataSheet_1_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.xlsx by Francesca Piludu (10706391)

“…The model with the final feature set was achieved using the support vector machine binary classification algorithm.</p>Results<p>Models for discriminating between Warthin’s and malignant tumors, benign and Warthin’s tumors and benign and malignant tumors had an accuracy of 86.7%, 91.9% and 80.4%, respectively. …”

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …”

Image_1_Validation of miRNA signatures for ovarian cancer earlier detection in the pre-diagnosis setting using machine learning approaches.pdf by Konrad Stawiski (4753380)

“…</p>Methods<p>We included a total of 80 ovarian cancer cases contributing 80 serum samples and compared 40 serum samples from cases with samples collected <2 years prior to diagnosis with 40 serum samples from cases with sample collection ≥2 to 7 years. …”

Data_Sheet_3_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.xlsx by Kirsty E. Waddington (5754545)

“…We tested the efficacy of six binary classification models using 10-fold cross validation; k-nearest neighbors, decision tree, random forest, support vector machine and lasso (Least Absolute Shrinkage and Selection Operator) logistic regression with and without interactions.…”

Image_1_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.JPEG by Kirsty E. Waddington (5754545)

“…We tested the efficacy of six binary classification models using 10-fold cross validation; k-nearest neighbors, decision tree, random forest, support vector machine and lasso (Least Absolute Shrinkage and Selection Operator) logistic regression with and without interactions.…”

Image_2_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.JPEG by Kirsty E. Waddington (5754545)

“…We tested the efficacy of six binary classification models using 10-fold cross validation; k-nearest neighbors, decision tree, random forest, support vector machine and lasso (Least Absolute Shrinkage and Selection Operator) logistic regression with and without interactions.…”

Data_Sheet_2_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.xlsx by Kirsty E. Waddington (5754545)

“…We tested the efficacy of six binary classification models using 10-fold cross validation; k-nearest neighbors, decision tree, random forest, support vector machine and lasso (Least Absolute Shrinkage and Selection Operator) logistic regression with and without interactions.…”

Data_Sheet_1_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.xlsx by Kirsty E. Waddington (5754545)

“…We tested the efficacy of six binary classification models using 10-fold cross validation; k-nearest neighbors, decision tree, random forest, support vector machine and lasso (Least Absolute Shrinkage and Selection Operator) logistic regression with and without interactions.…”