Subgroup analysis. by Sam Hall-McMaster (10343795)

Behavioral results. by Sam Hall-McMaster (10343795)

Learning dynamics on training tasks. by Sam Hall-McMaster (10343795)

Comparison of city decoders trained on session one and session two data. by Sam Hall-McMaster (10343795)

Validation tests of feature decoding during the associative memory paradigm. by Sam Hall-McMaster (10343795)

Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping by P. Vangeenderhuysen (15854812)

“…To automate targeted processing, we optimized an R-based targeted peak extraction (TaPEx) algorithm relying on a database comprising retention time and mass-to-charge ratio (360 metabolites and 132 lipids), with batch-specific quality control curation. …”

Portiloop implementation. by Nicolas Valenchon (13281855)

Human expert versus automated processing: Running dataset. by Keenon Werling (17512156)

SV detection workflow. by De-Min Duan (20685513)

IRBMO vs. variant comparison adaptation data. by 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. …”

A. Top – 14 days of continuous glucose monitor (CGM) data for a patient that spends all of their time within an optimal glucose range. by Jamison H. Burks (21029607)

Reconstruction results of phantom imaging. by Chi Zhang (9857)

Reconstruction results of a central slice of MPRAGE data at 3T. by Chi Zhang (9857)

Reconstruction results of a central slice of MPRAGE data at 7T. by Chi Zhang (9857)

Validation of image processing steps and metric results using images of a microfluidic device. by Gavrielle R. Untracht (11820311)

Pseudo Code of RBMO. by 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. …”

P-value on CEC-2017(Dim = 30). by 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. …”

Memory storage behavior. by 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. …”

Elite search behavior. by 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. …”

Description of the datasets. by 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. …”