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

Información del proyecto

LeSoDyMAS

Identificador del acuerdo de subvención: 659104

DOI

10.3030/659104

Proyecto finalizado el 23 Septiembre 2016

Fecha de la firma de la CE 23 Abril 2015

Fecha de inicio 13 Julio 2015

Fecha de finalización 12 Julio 2017

Financiado con arreglo a

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coste total

€ 183 454,80

Aportación de la UE

€ 183 454,80

Coordinado por

THE UNIVERSITY OF BIRMINGHAM
United Kingdom

Objetivo

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.

Ámbito científico (EuroSciVoc)

• ciencias sociales › sociología › demografía › mortalidad
• ciencias naturales › matemáticas › matemáticas aplicadas › sistemas dinámicos
• ciencias naturales › ciencias biológicas › genética › mutación
• ciencias naturales › informática y ciencias de la información › inteligencia artificial › aprendizaje automático
• ciencias naturales › ciencias biológicas › bioquímica › biomoléculas › proteínas › enzima

Palabras clave

• learning in the model space
• dynamical systems
• probabilistic modelling
• supervised distance learning
• model simplification
• Congenital Adrenal Hyperplasia
• pathway modes

Programa(s)

• H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions PROGRAMA PRINCIPAL
  
• H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
  

Tema(s)

• MSCA-IF-2014-EF - Marie Skłodowska-Curie Individual Fellowships (IF-EF)
  

Régimen de financiación

MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

Convocatoria de propuestas

H2020-MSCA-IF-2014

Coordinador

THE UNIVERSITY OF BIRMINGHAM

• Aportación neta de la UEn: € 183 454,80
• Dirección: Edgbaston ; B15 2TT Birmingham ; Reino Unido
• Región: West Midlands (England) › West Midlands › Birmingham
• Tipo de actividad: Higher or Secondary Education Establishments
• Coste total: € 183 454,80