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- 19:47, 11 November 2025 Volunteership Spring 2026 (hist | edit) [11,104 bytes] Hivelabwikiadmin (talk | contribs) (Created page with "vdv")
- 17:19, 24 September 2025 Getting Recommendations from HIVE Lab Faculty and Staff (hist | edit) [1,424 bytes] Rehmam (talk | contribs) (Created page with "If you are a current or former member of the HIVE Lab and would like to request a recommendation, please follow the steps below. Providing complete and accurate information will help us prepare a stronger and more personalized letter on your behalf. == Steps for Requesting a Recommendation == '''Step 1. Submit the Request''' Send all recommendation requests from each organization to: '''mazumder_lab@gwu.edu''' '''Step 2. Updated LinkedIn Profile''' Include a link to...") Tag: Visual edit
- 17:35, 10 September 2025 Metagenomic resources (hist | edit) [10,449 bytes] Jkeeney (talk | contribs) (Built page for representing all Mazumder lab metagenomic resources)
- 13:59, 29 August 2025 METAGENOMICS WIKI (hist | edit) [301 bytes] Mazumder (talk | contribs) (New page) Tag: Visual edit
- 18:38, 28 August 2025 Model Training and Validation (hist | edit) [8,408 bytes] Lorikrammer (talk | contribs) (Created page with "The initial cohort of models submitted along with the initial release of the PredictMod platform were built using a wide variety of techniques for handling every stage, from data ingestion through modeling. Such techniques included: K-nearest neighbors, Synthetic Neighbor Oversampling Technique (SMOTE), and Leave-One-Out-Cross-Validation (LOOCV) (for data sampling and harmonization); conditional Generative Adversarial Networks (cGANs), SMOTE, multivariate kernel density...") Tag: Visual edit
- 18:23, 28 August 2025 Augmenting real data with synthetic data (hist | edit) [3,742 bytes] Lorikrammer (talk | contribs) (Created page with "In biomedical research, small sample sizes often pose challenges for developing robust machine learning models and evaluating computational scalability. To overcome this limitation, we have designed an algorithm that utilizes conditional Generative Adversarial Networks (cGANs) to generate synthetic data, effectively expanding available datasets. While synthetic data may not always improve model accuracy, it provides researchers with the ability to assess computational ef...") Tag: Visual edit