Skip to main content

Machine learning for quantitative modelling of structured phenotypes

Objetivo

Understanding phenotypic variation, and more particularly identifying the causal genetic or environmental regulators, is a major aim in biological investigations. The goal of this proposal is to develop and apply machine learning techniques to model key aspects of structure that occur in modern, high-dimensional phenotype datasets. First, the temporal structure of phenotypes that are recorded over time is addressed. Statistical models can exploit smoothness of time series and detect change points. Second, the structure of images, arising when digital pictures are used as phenotypic variables, is considered. Machine learning techniques allow interpretable image features to be automatically extracted and used as quantitative traits, complementing classical measurements. Finally, the network structure of the phenome is addressed. Different phenotype variables influence each other, resulting in a chain of effects that needs to be modelled to reveal the true causal relationships. The developed algorithms will be applied to understand phenotypic variation in Arabidopsis thaliana in direct collaboration with researchers at the Max Planck Institute for Developmental Biology.

Convocatoria de propuestas

FP7-PEOPLE-2009-IEF
Consulte otros proyectos de esta convocatoria

Coordinador

EUROPEAN MOLECULAR BIOLOGY LABORATORY
Dirección
Meyerhofstrasse 1
69117 Heidelberg
Alemania

Ver en el mapa

Tipo de actividad
Research Organisations
Contacto administrativo
Tom Ratcliffe (Mr.)
Aportación de la UE
€ 154 460,99

Participantes (1)

MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV

La participación finalizó

Alemania
Dirección
Hofgartenstrasse 8
80539 Munchen

Ver en el mapa

Tipo de actividad
Research Organisations
Contacto administrativo
Patrice Wegener (Mr.)