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

“…From this dataset we can compute the MI between each interaction and the outcome and <b>(D)</b> obtain a ranked list (<i>I</i>_<i>δ</i>) based on this metric. <b>(E)</b> Starting from the interaction at the top of <i>I</i>_<i>δ</i>, <i>hi</i>PRS constructs <i>I</i>_<i>K</i>, selecting <i>K</i> (where <i>K</i> is user-specified) terms through the greedy optimization of the ratio between MI (<i>relevance</i>) and a suitable measure of similarity for interactions (<i>redundancy)</i> (cf. …”

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

Subjects:

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

Simulation parameters. by Hend Bayoumi (22693738)

Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations. by 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. by Yi-Wen Hsiao (13767916)

Subjects:

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

Subjects:

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

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

Subjects:

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

Subjects:

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

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

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

A graphic overview illustrates the random walk and permutation test in the <i>ceRNApairFiltering</i> method. by Yi-Wen Hsiao (13767916)

Subjects:

A graphic overview illustrates sorting and correlation calculation in the <i>ceRNApairFiltering</i> method. by Yi-Wen Hsiao (13767916)

Subjects:

Run time comparisons between published tools and our package. by Yi-Wen Hsiao (13767916)

Subjects: