S. Reel

发表

Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven study

F. Beuschlein, G. Assié, J. McClure, 2022, EBioMedicine.

Whole blood methylome-derived features to discriminate endocrine hypertension

F. Beuschlein, J. Bertherat, G. Assié, 2022, Clinical Epigenetics.

Predicting Hypertension Subtypes with Machine Learning Using Targeted Metabolites and Their Ratios

Emily R. Jefferson, F. Beuschlein, G. Assié, 2022, Metabolites.

GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)

Alberto Blanco-Justicia, J. Domingo-Ferrer, E. Jefferson, 2022, ArXiv.

Using machine learning approaches for multi-omics data analysis: A review.

E. Trucco, E. Jefferson, P. Reel, 2021, Biotechnology advances.

Targeted Metabolomics as a Tool in Discriminating Endocrine From Primary Hypertension

F. Beuschlein, G. Assié, J. Adamski, 2020, The Journal of clinical endocrinology and metabolism.

Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension

F. Beuschlein, M. Fassnacht, J. Adamski, 2022, Metabolites.

Artificial Neural Networks and Fuzzy Logic in Process Modeling and Control

Ashok K. Goel, Ashok Kumar Goel, Smarti Reel, 2011 .

A novel inpainting framework for virtual view synthesis

Smarti Reel, S. Reel, 2017 .

Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities

E. Jefferson, F. Ritchie, Simon Rogers, 2021, Heliyon.