Opendata, web and dolomites


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

Total Cost €


EC-Contrib. €






 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.

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

Project "LeSoDyMAS" data sheet

The following table provides information about the project.


Organization address
address: Edgbaston
postcode: B15 2TT

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
 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


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 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.


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

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "LESODYMAS" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email ( and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

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

More projects from the same programme (H2020-EU.1.3.2.)

NeuroSens (2019)

Neuromodulation of Sensory Processing

Read More  

NaWaTL (2020)

Narrative, Writing, and the Teotihuacan Language: Exploring Language History Through Phylogenetics, Epigraphy and Iconography

Read More  

NPsVLCD (2019)

Natural Product-Inspired Therapies for Leishmaniasis and Chagas Disease

Read More