A sample confusion matrix of binary classifier. by Eric Komla Anku (17959299)

The workflow of SCARS-Logistic algorithm. by Maryam Sadiq (7046903)

Performance of the ceRNAR package using synthetic data with randomly selected correlation values from 0.8 to 0.9. by Yi-Wen Hsiao (13767916)

Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.3 to 0.9. by Yi-Wen Hsiao (13767916)

Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.5 to 0.7. by Yi-Wen Hsiao (13767916)

Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.3 to 0.4. by Yi-Wen Hsiao (13767916)

An illustrated example of Algorithm 1 (Fig 1). by Kathleen M. Jagodnik (10170617)

Link Prediction for Egocentrically Sampled Networks by Tianxi Li (8460468)

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

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

Smaller gene set of DFC was selected by a unique selection criterion. by Takeru Fujii (11731550)

The basic concept of our idea is illustrated graphically. by Yi-Wen Hsiao (13767916)

Benchmarking of algorithms for unsupervised clustering of multi-omics data. by Polina Suter (11879717)

Modeling Pregnancy Outcomes Through Sequentially Nested Regression Models by Xuan Bi (3096897)

“…Our approach explicitly bridges the connections across nested outcomes through computationally easy algorithms and enjoys theoretical guarantee of estimation and variable selection. …”

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. …”

Illustration of the workflow combining BayesFlow with missing data encoding. by Zijian Wang (481291)

UNet++ type architecture [68] used for all the problems in this paper. by Dylan D. Ray (3741823)

Interpretability analysis. by Michela C. Massi (14599915)

Data generation process. by Nicola Dietler (12551766)

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)

“…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …”