Obiettivo 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. Campo scientifico natural sciencesbiological sciencesdevelopmental biologynatural sciencesmathematicsapplied mathematicsstatistics and probabilitynatural sciencescomputer and information sciencesartificial intelligencemachine learning Programma(i) FP7-PEOPLE - Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) Argomento(i) FP7-PEOPLE-2009-IEF - Marie Curie Action: "Intra-European Fellowships for Career Development" Invito a presentare proposte FP7-PEOPLE-2009-IEF Vedi altri progetti per questo bando Meccanismo di finanziamento MC-IEF - Intra-European Fellowships (IEF) Coordinatore EUROPEAN MOLECULAR BIOLOGY LABORATORY Contributo UE € 154 460,99 Indirizzo Meyerhofstrasse 1 69117 Heidelberg Germania Mostra sulla mappa Regione Baden-Württemberg Karlsruhe Heidelberg, Stadtkreis Tipo di attività Research Organisations Contatto amministrativo Tom Ratcliffe (Mr.) Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Costo totale Nessun dato Partecipanti (1) Classifica in ordine alfabetico Classifica per Contributo UE Espandi tutto Riduci tutto MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV Partecipazione conclusa Germania Contributo UE Nessun dato Indirizzo HOFGARTENSTRASSE 8 80539 Munchen Mostra sulla mappa Regione Bayern Oberbayern München, Kreisfreie Stadt Tipo di attività Research Organisations Contatto amministrativo Patrice Wegener (Mr.) Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Costo totale Nessun dato
