Novel Distributed Informatics Platform to Support Machine Learning Discovery of Metabolic Biomarkers in Hypoxia Predisposition

Anthony Stell, Vedant Chauhan, Sandra Amador, Felix Beuschlein, Judith Favier, David Gil, Philip Greenwood, Ronald de Krijger, Matthias Kroiss, Samanta Ortuno, Attila Patocs, Axel Walch

2023

Abstract

To realise the scientific and clinical benefits of machine learning (ML) in a multi-centre research collaboration, a common issue is the need to bring high-volume data, complex analytical algorithms, and large-scale processing power, all together into one place. This paper describes the detailed architecture of a novel platform that combines these features, in the context of a proposed new clinical/bioinformatics project, Hypox-PD. Hypox-PD uses ML methods to identify new metabolic biomarkers, through the analysis of high-volume data including mass spectrometry and imaging morphology of biobank tissue. The platform features three components: a content delivery network (CDN); a standardised orchestration application; and high-specification processing power and storage. The central innovation of this platform is a distributed application that simultaneously manages the workflow between these components, provides a virtual mapping of the domain data dictionary, and presents the project data/metadata in a FAIR-compliant external interface. This paper presents the detailed design specifications of this platform, as well as initial test results in establishing the benchmark challenge of current direct transfer times without any specialised support. An initial costing of CDN usage is also presented, which indicates that significant performance improvement may be achievable at a reasonable cost to research budgets.

Paper Citation

in Harvard Style

Stell A., Chauhan V., Amador S., Beuschlein F., Favier J., Gil D., Greenwood P., de Krijger R., Kroiss M., Ortuno S., Patocs A. and Walch A. (2023). Novel Distributed Informatics Platform to Support Machine Learning Discovery of Metabolic Biomarkers in Hypoxia Predisposition. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF; ISBN 978-989-758-631-6, SciTePress, pages 105-116. DOI: 10.5220/0011665100003414

in Bibtex Style

@conference{healthinf23,
author={Anthony Stell and Vedant Chauhan and Sandra Amador and Felix Beuschlein and Judith Favier and David Gil and Philip Greenwood and Ronald de Krijger and Matthias Kroiss and Samanta Ortuno and Attila Patocs and Axel Walch},
title={Novel Distributed Informatics Platform to Support Machine Learning Discovery of Metabolic Biomarkers in Hypoxia Predisposition},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF},
year={2023},
pages={105-116},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011665100003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF
TI - Novel Distributed Informatics Platform to Support Machine Learning Discovery of Metabolic Biomarkers in Hypoxia Predisposition
SN - 978-989-758-631-6
AU - Stell A.
AU - Chauhan V.
AU - Amador S.
AU - Beuschlein F.
AU - Favier J.
AU - Gil D.
AU - Greenwood P.
AU - de Krijger R.
AU - Kroiss M.
AU - Ortuno S.
AU - Patocs A.
AU - Walch A.
PY - 2023
SP - 105
EP - 116
DO - 10.5220/0011665100003414
PB - SciTePress