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LeSoDyMAS

Learning in the Space of Dynamical Models of Adrenal Steroidogenesis “LeSoDyMAS”

Total Cost €

EC-Contrib. €

Partnership

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Outcomes and
results

LeSoDyMAS project word cloud

Explore the words cloud of the LeSoDyMAS project. It provides you a very rough idea of what is the project "LeSoDyMAS" about.

data natural framework interpret simplification generalised prof expert flow probabilistic learning model company subsequently uob distributions vectorial paradigm techniques expertise clinical posterior date few domain treatment combine tino warwick individual dynamical successfully rarely machine technique underlying amount steroidogenesis difficult incorporation cah deeper judge potentially bunte interdisciplinary adrenal models preprocessing trained dimensionality modules representing medical sheffield pathophysiologic biological black hyperplasia patient box dr appears inconvenience congenital complicated prediction incorporating steroid diurnal fellow predominantly host amounts ltd similarity performance input formulation successful statistical birmingham university limited space combines collaborated bio

Project "LeSoDyMAS" data sheet

The following table provides information about the project.

Coordinator	THE UNIVERSITY OF BIRMINGHAM Organization address address: Edgbaston city: BIRMINGHAM postcode: B15 2TT website: www.bham.ac.uk contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.
Coordinator Country	United Kingdom [UK]
Project website	http://www.cs.rug.nl/
Total cost	183˙454 €
EC max contribution	183˙454 € (100%)
Programme	1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
Code Call	H2020-MSCA-IF-2014
Funding Scheme	MSCA-IF-EF-ST
Starting year	2015
Duration (year-month-day)	from 2015-07-13 to 2017-07-12

Partnership

Take a look of project's partnership.

#	participants	country	role	EC contrib. [€]
1	THE UNIVERSITY OF BIRMINGHAM THE UNIVERSITY OF BIRMINGHAM Organization address address: Edgbaston city: BIRMINGHAM postcode: B15 2TT website: www.bham.ac.uk contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	UK (BIRMINGHAM)	coordinator	183˙454.00

Map

Project objective

To date most successful machine learning techniques for the analysis of complex interdisciplinary data predominantly use significant amounts of vectorial measurements as input to a statistical system. The domain expert knowledge is often only used in data preprocessing and the subsequently trained technique appears as a black-box, which is difficult to interpret or judge and rarely allows insight into the underlying natural process. However, in many bio-medical applications the underlying biological process is complex and the amount of measurements is limited due to the costs and inconvenience for the patient. The main aim of this project is the formulation of a generalised framework for learning in the space of probabilistic models representing the complicated underlying natural processes with potentially very few measurements. This includes the development of a similarity measure for posterior distributions, task-driven model simplification and a new learning paradigm to combine those modules. The method will be developed for the prediction of steroid flow in the treatment of Congenital Adrenal Hyperplasia (CAH) incorporating dynamical models of Adrenal Steroidogenesis. With the successful execution of this project we expect not only better prediction performance for individual treatment success, but also deeper understanding of the pathophysiologic processes due to the incorporation of the pathway models. The project combines the expertise of the Fellow (Dr. Bunte) in task-driven similarity learning and dimensionality reduction with the expertise of the Host Coordinator (Prof. Tino, The University of Birmingham (UoB)) in probabilistic modelling, dynamical systems and model-based learning. The UoB and all participants (University of Sheffield,Warwick and the company Diurnal Ltd) provide further bio-medical and modelling expertise, and have already successfully collaborated in previous projects, including the clinical data targeted in this proposal.

Publications

List of publications.
year	authors and title	journal	last update
2016	Kerstin Bunte and Elizabeth S. Baranowski and Wiebke Arlt and Peter Tino Relevance Learning Vector Quantization in Variable Dimensional Spaces published pages: 20-23, ISSN: , DOI:	New Challenges in Neural Computation NC^2 Workshop of the GI-Fachgruppe N	2019-07-24

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The information about "LESODYMAS" are provided by the European Opendata Portal: CORDIS opendata.