Omics-Based Strategies for Improved Diagnosis and Treatment of Endocrine Hypertension (ENSAT-HT) (Joint with Inserm, University of Torino, University of Padua, University of Glasgow, University of Birmingham, Radboud University Medical Centre, SleekIT Limited and Inserm Transfert)

Connell, John (Investigator)
Doney, Alexander (Investigator)
Jefferson, Emily (Investigator)
Zhou, Kaixin (Investigator)

Status	Finished
Effective start/end date	1/05/15 → 31/12/21

Funding

COMMISSION OF THE EUROPEAN COMMUNITIES (H2020-PHC-2014-2015): £724,921.00

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

Health Informatics Centre
Health and Clinical Services
Facility/equipment: Facility

Researchers develop new system to classify endocrine hypertension
Reel, P., Reel, S. & Jefferson, E.
12/10/22
1 item of Media coverage
Press/Media: Research

2 Invited talk
1 Membership of network

Machine learning for classification of hypertension subtypes using multi-omics
Reel, P. (Speaker)
12 Oct 2022
Activity: Talk or presentation types › Invited talk

File
Machine Learning Approaches for Multi-Omics Data Analysis
Reel, P. (Speaker)
18 Oct 2022
Activity: Talk or presentation types › Invited talk
european network for the study of adrenal tumors (External organisation)
Reel, P. (Member)
1 Mar 2016 → 15 Jan 2026
Activity: Membership types › Membership of network

8 Article
1 Poster
1 Meeting abstract
1 Review article

Identification of hypertension subtypes using microRNA profiles and machine learning
Reel, S., Reel, P. S., Van Kralingen, J., Larsen, C. K., Robertson, S., MacKenzie, S. M., Riddell, A., McClure, J. D., Lamprou, S., Connell, J. M. C., Amar, L., Pecori, A., Tetti, M., Pamporaki, C., Kabat, M., Ceccato, F., Kroiss, M., Dennedy, M. C., Stell, A. & Deinum, J. & 11 others, Mulatero, P., Reincke, M., Gimenez-Roqueplo, A.-P., Assié, G., Blanchard, A., Beuschlein, F., Rossi, G.-P., Eisenhofer, G., Zennaro, M. C., Jefferson, E. & Davies, E. (Lead / Corresponding author), 19 Mar 2025, (E-pub ahead of print) In: European Journal of Endocrinology. lvaf052.
Research output: Contribution to journal › Article › peer-review

Open Access
File
2 Downloads (Pure)
Classifying hypertension subtypes using multi-omics: A multi-centre machine learning based retrospective study
Reel, P. S., Reel, S., Cole, C., Zennaro, M. C. & Jefferson, E., 31 Oct 2023. 1 p.
Research output: Contribution to conference › Poster › peer-review

Open Access
FRI135 Differential Expression Of Circulating MicroRNAs In Primary Aldosteronism: Potential Biomarkers For Improved Diagnosis
Lamprou, S., MacKenzie, S. M., McClure, J. D., Robertson, S., Riddell, A., van Kralingen, J. C., Gimenez-Roqueplo, A.-P., Reel, P., Reel, S., Assié, G., Amar, L., Beuschlein, F., Rossi, G. P., Deinum, J., Reincke, M., Eisenhofer, G., Jefferson, E., Zennaro, M. C. & Davies, E., Oct 2023, In: Journal of the Endocrine Society. 7, Suppl 1, p. A350-A351 2 p., bvad114.647.
Research output: Contribution to journal › Meeting abstract › peer-review

Open Access
File

3 Downloads (Pure)

ENSAT Scientific Award
Reel, P. (Recipient) & Reel, S. (Recipient), 2023
Prize: Prize (including medals and awards)

Omics-Based Strategies for Improved Diagnosis and Treatment of Endocrine Hypertension (ENSAT-HT) (Joint with Inserm, University of Torino, University of Padua, University of Glasgow, University of Birmingham, Radboud University Medical Centre, SleekIT Limited and Inserm Transfert)

Project Details

Funding

UN Sustainable Development Goals

Fingerprint

Health Informatics Centre

Researchers develop new system to classify endocrine hypertension

Machine learning for classification of hypertension subtypes using multi-omics

Machine Learning Approaches for Multi-Omics Data Analysis

european network for the study of adrenal tumors (External organisation)

Identification of hypertension subtypes using microRNA profiles and machine learning

Classifying hypertension subtypes using multi-omics: A multi-centre machine learning based retrospective study

FRI135 Differential Expression Of Circulating MicroRNAs In Primary Aldosteronism: Potential Biomarkers For Improved Diagnosis

ENSAT Scientific Award

Omics-Based Strategies for Improved Diagnosis and Treatment of Endocrine Hypertension (ENSAT-HT) (Joint with Inserm, University of Torino, University of Padua, University of Glasgow, University of Birmingham, Radboud University Medical Centre, SleekIT Limited and Inserm Transfert)

Project Details

Funding

UN Sustainable Development Goals

Fingerprint

Equipment

Press/Media

Activities

Research output

Prizes