The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. حسب Olaide N. Oyelade (14047002)

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

Flowchart of the general algorithm for one trial. حسب Junying Wang (509009)

Simulation parameters. حسب Hend Bayoumi (22693738)

Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations. حسب Tristan Mary-Huard (3864)

The performance of our proposed method in four scenarios (I to IV) uses simulated data that mimics real-world data distribution with the complete version. حسب Yi-Wen Hsiao (13767916)

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Performance of our proposed method in four scenarios (I to IV) using naïve simulated data with the complete version. حسب Yi-Wen Hsiao (13767916)

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

The Pseudo-Code of the IRBMO Algorithm. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

Joint Detection of Change Points in Multichannel Single-Molecule Measurements حسب Hugh Wilson (11797444)

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An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA. حسب Olaide N. Oyelade (14047002)

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IRBMO vs. meta-heuristic algorithms boxplot. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

IRBMO vs. feature selection algorithm boxplot. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

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

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

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Performance comparisons between ceRNIA, JAMI, SPONGE, GDCRNATools and ceRNAR packages using synthetic data that mimic the real-world case. حسب Yi-Wen Hsiao (13767916)

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Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with a fixed correlation value (0.9). حسب Yi-Wen Hsiao (13767916)

الموضوعات:

Performance of the ceRNAR package in five scenarios under the fast version using synthetic data with a fixed correlation value (0.9). حسب Yi-Wen Hsiao (13767916)

الموضوعات:

Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with a fixed correlation value (0.6). حسب Yi-Wen Hsiao (13767916)

الموضوعات:

Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with a fixed correlation value (0.3). حسب Yi-Wen Hsiao (13767916)

الموضوعات: