Search research articles
Contact Us
Filters
Showing results (1-10 of 10) with videos related to
Page
of 1
Sort By:
Biotechnology Advances
|
April 1, 2021
Using machine learning approaches for multi-omics data analysis: A review
Parminder 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 opportunities
Esma 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 data
Parminder 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 Hypertension
Zoran Erlic, Parminder Reel, Smarti Reel, et al.
Metabolites
|
August 25, 2022
Predicting Hypertension Subtypes with Machine Learning Using Targeted Metabolites and Their Ratios
Smarti 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 Records
Rob Baxter, Thomas Nind, James Sutherland, et al.
European Journal of Endocrinology
|
March 19, 2025
Identification of hypertension subtypes using microRNA profiles and machine learning
Smarti Reel, Parminder S Reel, Josie Van Kralingen, et al.
Clinical Epigenetics
|
November 4, 2022
Whole blood methylome-derived features to discriminate endocrine hypertension
Roberta Armignacco, Parminder S Reel, Smarti Reel, et al.
Metabolites
|
July 27, 2022
Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension
Nikolaos 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 study
Parminder S Reel, Smarti Reel, Josie C van Kralingen, et al.
Page
of 1
Search research articles
Search
Showing results (1-10 of 10) with videos related to
Sort By:
Page
of 1
Biotechnology Advances
|
April 1, 2021
Using machine learning approaches for multi-omics data analysis: A review
Parminder 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 opportunities
Esma 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 data
Parminder 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 Hypertension
Zoran Erlic, Parminder Reel, Smarti Reel, et al.
Metabolites
|
August 25, 2022
Predicting Hypertension Subtypes with Machine Learning Using Targeted Metabolites and Their Ratios
Smarti 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 Records
Rob Baxter, Thomas Nind, James Sutherland, et al.
European Journal of Endocrinology
|
March 19, 2025
Identification of hypertension subtypes using microRNA profiles and machine learning
Smarti Reel, Parminder S Reel, Josie Van Kralingen, et al.
Clinical Epigenetics
|
November 4, 2022
Whole blood methylome-derived features to discriminate endocrine hypertension
Roberta Armignacco, Parminder S Reel, Smarti Reel, et al.
Metabolites
|
July 27, 2022
Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension
Nikolaos 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 study
Parminder S Reel, Smarti Reel, Josie C van Kralingen, et al.
Page
of 1