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LeSoDyMAS

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

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 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.

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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.
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 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 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

year authors and title journal last update
List of publications.
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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