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Smarti Reel

Showing results (1-10 of 10) with videos related to

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Biotechnology Advances|April 1, 2021
Using machine learning approaches for multi-omics data analysis: A reviewParminder S Reel, Smarti Reel, Ewan Pearson, et al.
Heliyon|May 1, 2023
Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunitiesEsma Mansouri-Benssassi, Simon Rogers, Smarti Reel, et al.
Alzheimer'S Research & Therapy|May 22, 2026
Predicting future dementia from routine clinical MRI and linked healthcare dataParminder Singh Reel, Salim Al-Wasity, Craig Edwards, et al.
The Journal of Clinical Endocrinology and Metabolism|December 31, 2020
Targeted Metabolomics as a Tool in Discriminating Endocrine From Primary HypertensionZoran Erlic, Parminder Reel, Smarti Reel, et al.
Metabolites|August 25, 2022
Predicting Hypertension Subtypes with Machine Learning Using Targeted Metabolites and Their RatiosSmarti Reel, Parminder S Reel, Zoran Erlic, et al.
Radiology. Artificial Intelligence|January 2, 2024
The Scottish Medical Imaging Archive: 57.3 Million Radiology Studies Linked to Their Medical RecordsRob Baxter, Thomas Nind, James Sutherland, et al.
European Journal of Endocrinology|March 19, 2025
Identification of hypertension subtypes using microRNA profiles and machine learningSmarti Reel, Parminder S Reel, Josie Van Kralingen, et al.
Clinical Epigenetics|November 4, 2022
Whole blood methylome-derived features to discriminate endocrine hypertensionRoberta Armignacco, Parminder S Reel, Smarti Reel, et al.
Metabolites|July 27, 2022
Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine HypertensionNikolaos G Bliziotis, Leo A J Kluijtmans, Gerjen H Tinnevelt, et al.
Ebiomedicine|September 30, 2022
Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven studyParminder S Reel, Smarti Reel, Josie C van Kralingen, et al.
Pageof 1

Showing results (1-10 of 10) with videos related to

Sort By:
Pageof 1
Biotechnology Advances|April 1, 2021
Using machine learning approaches for multi-omics data analysis: A reviewParminder S Reel, Smarti Reel, Ewan Pearson, et al.
Heliyon|May 1, 2023
Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunitiesEsma Mansouri-Benssassi, Simon Rogers, Smarti Reel, et al.
Alzheimer'S Research & Therapy|May 22, 2026
Predicting future dementia from routine clinical MRI and linked healthcare dataParminder Singh Reel, Salim Al-Wasity, Craig Edwards, et al.
The Journal of Clinical Endocrinology and Metabolism|December 31, 2020
Targeted Metabolomics as a Tool in Discriminating Endocrine From Primary HypertensionZoran Erlic, Parminder Reel, Smarti Reel, et al.
Metabolites|August 25, 2022
Predicting Hypertension Subtypes with Machine Learning Using Targeted Metabolites and Their RatiosSmarti Reel, Parminder S Reel, Zoran Erlic, et al.
Radiology. Artificial Intelligence|January 2, 2024
The Scottish Medical Imaging Archive: 57.3 Million Radiology Studies Linked to Their Medical RecordsRob Baxter, Thomas Nind, James Sutherland, et al.
European Journal of Endocrinology|March 19, 2025
Identification of hypertension subtypes using microRNA profiles and machine learningSmarti Reel, Parminder S Reel, Josie Van Kralingen, et al.
Clinical Epigenetics|November 4, 2022
Whole blood methylome-derived features to discriminate endocrine hypertensionRoberta Armignacco, Parminder S Reel, Smarti Reel, et al.
Metabolites|July 27, 2022
Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine HypertensionNikolaos G Bliziotis, Leo A J Kluijtmans, Gerjen H Tinnevelt, et al.
Ebiomedicine|September 30, 2022
Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven studyParminder S Reel, Smarti Reel, Josie C van Kralingen, et al.
Pageof 1