Results from applying our algorithms to the 9 real-life networks. by Helal El-Zaatari (20442243)

Success convergence rates of Method 2 and our method. by Huanyu Yang (6683780)

The selection algorithm flowchart. by Shiyu Wang (813972)

Diagrammatic representation of the overall workflow of our experiment to select the optimum model. by Giuliano Lorenzoni (21440268)

Execution time for Method 1, Method 2, and our method (second). by Huanyu Yang (6683780)

Summary of the feature selection algorithms. by Martin Kostadinov (20410120)

UpSet diagram illustrating the overlap of DNBs selected by our method and top-ranked genes by the 13 baseline centrality-based ranking algorithms. by Fatemeh Keikha (20917848)

True versus fitted (standardized) BMI from BGLR model and our model (HighDimMixedModels with SCAD penalty) with and without random effects included. by Evan Gorstein (20553314)

Feature selection using the Boruta algorithm. by Guang Tu (22054865)

“…We extracted baseline characteristics, laboratory parameters, and clinical outcomes. The Boruta algorithm was employed for feature selection to identify variables significantly associated with in-hospital mortality, and 16 machine learning models, including logistic regression, random forest, gradient boosting, and neural networks, were developed and compared using receiver operating characteristic (ROC) curves and area under the curve (AUC) analysis. …”

Research process of our study. by Devi Soorya Narayana Sasikumar (22331743)

Comparison of classification accuracy of our developed method and existing channel selection methods of dataset 3. by Taslima Khanam (9739867)

Comparison of classification accuracy of our developed method and existing channel selection methods of dataset 2. by Taslima Khanam (9739867)

Comparison of classification accuracy of our developed method and existing channel selection methods of dataset 1. by Taslima Khanam (9739867)

Comparison of related works that use the same dataset used in our study of not. by Nour Eldeen Mahmoud Khalifa (19259450)

Nonparametric Assessment of Variable Selection and Ranking Algorithms by Zhou Tang (7309340)

“…In this article, we propose methods of comparing variable selection and ranking algorithms. We first introduce measures of the quality of variable selection and ranking algorithms. …”

Overview of our approach. by Carlos A. Gomez-Uribe (21492788)

Select results from algorithmic differentiation of consistent from transient mitochondrial dysfunction identified using our hierarchical MMM-based classification protocol (with a trigger of LPR > 25) (Fig 1). by Michael S. Baker (22170470)

This figure shows a non-exhaustive representation of the main models reported in our discussion, for illustrative purpose only. by Giuseppe Agapito (104634)

Convergence curves for the applied algorithms. by Eslam Hamouda (5172083)

Comparison of related studies with our work. by Zhipeng Huang (1759759)