PredictMod: Difference between revisions
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*[[PredictMod Machine-Learning Pipeline Tutorial|PredictMod Machine Learning Pipeline Tutorial]] | *[[PredictMod Machine-Learning Pipeline Tutorial|PredictMod Machine Learning Pipeline Tutorial]] | ||
*[[How to Find and Extract Machine-Usable Data from Scientific Literature]] | *[[How to Find and Extract Machine-Usable Data from Scientific Literature]] | ||
*[[Frequently Asked Questions]] | *[[PredictMod Frequently Asked Questions|Frequently Asked Questions]] | ||
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Revision as of 19:35, 12 March 2025
About
PredictMod (https://hivelab.biochemistry.gwu.edu/predictmod) is an application designed to provide clinicians with a powerful decision making tool that enhances clinical understanding of patient-level data. Through the use of the open-source PredictMod platform, clinicians, patients, and researchers will access predictive ML models based on real-world data. The platform empowers users with limited experience in bioinformatics to leverage the power of predictive modeling, providing a collaborative solution for improving patient outcomes. The PredictMod platform utilizes ML tools and complex datasets based on electronic medical records (EMR), gut microbiome, and other -omics data to forecast patient outcomes, often in response to treatment for a particular condition.
While our primary conditions of interest are prediabetes and cancer, the tool is designed to be used for a variety of conditions, interventions, and data types. The agnostic nature of the platform allows for widespread use and relevance to all fields within the scope of medicine.
About HIVE Lab
The HIVE Lab group is involved in developing the High-performance Integrated Virtual Environment (HIVE) which aims to integrate various high throughput data analysis tools for bioinformatics. In addition to the HIVE platform, the lab is involved in developing bioinformatics tools and resources, such as standards for bioinformatics communication, knowledgebases for glycoinformatics and infectious diseases, cancer research, and microbiome analysis.
Publications
- Krammer L, Aggarwal V, Bhuiyan U, McNeely P, Mazumder R. PredictMod: a machine learning-based platform for predicting and sharing intervention outcomes in patients. Poster presented at: 22nd International Conference on Artificial Intelligence in Medicine; July 9-12, 2024; Salt Lake City, Utah, USA.
- Wu J, Singleton SS, Bhuiyan, Krammer L, Mazumder R. Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning. Front. Mol. Biosci.. 19 January 2024; Sec. Molecular Diagnostics and Therapeutics. Volume 10 – 2023. PMID: 38313584.
- Bhuiyan, U. in Biochemistry and Molecular Medicine, Vol. Masters 72 (George Washington University, Washington, DC; 2023).
- Dahlin M, Singleton S, David J, Basuchoudhary A, Wickstrom, Mazumder R, Prast-Nielsen S. Higher levels of Bifidobacteria and tumor necrosis factor in children with drug-resistant epilepsy are associated with anti-seizure response to the ketogenic diet. eBioMedicine. June 2022; vol: 80. https://doi.org/10.1016/j.ebiom.2022.104061.
- Hopson L, Singleton S, David J, Basuchoudhary A, Prast-Nielsen S, Klein P, Sen S, Mazumder R. Bioinformatics and machine learning in gastrointestinal microbiome research and clinical application. Prog Mol Biol Transl Sci. 2020 Sep 30; 176:141-178. PMID: 33814114.
- King CH, Desai H, Sylvetsky AC, LoTempio J, Ayanyan S, Carrie J, Crandall K, Fochtman B, Gasparyan L, Gulzar N, Howell P, Issa N, Krampis K, Mishra L, Morizono H, Pisegna JR, Rao S, Ren Y, Simonyan V, Smith K, VedBrat S, Yao M, Mazumder R. Baseline human gut microbiota profile in healthy people and standard reporting template. PLOS ONE. 2019. PMID: 31509535.
MultiMedia
- Microbiome: VA AI Tech Sprint 2021 | Phase 2 Demo Featuring Stephanie Singleton, Edited by James Ziegler Published December 8th, 2020
Phase 2 Demo. View our MATLAB Prototype Demo. View our Phase 2 Demo Slides. This video is a part of the Microbiome Project.
- VA AI Tech Sprint Phase 3 Final Demo | GWU HIVE Presented by Stephanie Singleton, James Ziegler, Edited by James Ziegler Published April 20th, 2021
Phase 3 Demo. View our materials. This video is a part of the Microbiome Project.
Current and Former Contributors
The George Washington University
Raja Mazumder
Pat McNeely
Urnisha Bhuiyan
Lori Krammer
Miguel Mazumder
External Collaborators
Sabyasachi Sen, Veterans Administration
Jorge Sepulveda, Medical Faculty Associates
Atin Basu Choudhary, Virginia Military Institute
John David, Virginia Military Institute
Vinod Aggarwal, Veterans Administration
Former Contributors
Abel Argaw
Stephanie Singleton
Sangeeta Agarwal
Zacharie Savarie
Janet Chrosniak
Josh Hakakian
Nicole Richmond
Wilma Jogunoori
Arad Jain
Hadley King
Robel Kahsay
Special thanks to our interns and volunteers.