Grant: Discovery Grants Program - Individual
HEC Montréal
$22,000.00
Component-based methods for data reduction and integration: application to genomics and neuroimaging studies.
EF 2019-20
Résumé
It is now well accepted that the biggest challenges and upcoming breakthroughs in the genomic field will be derived from handling big data. Not only is big data generated faster than our own capacity to fully understand, interpret and evaluate the results produced, it is also collected from an ever-increasing number of sources. As a result, statistical challenges with integrating this information into a unified model of genome function needs to be addressed. With a particular focus on genomic studies, the proposed research endeavours to develop a set of new statistical tools designed to investigate association between two or more sets of possibly high dimensional correlated variables measured on the same subjects. In particular, the general objective of the proposal is to tackle both the issue of data reduction and association analysis simultaneously, by developing statistical models achieving an optimal data reduction towards the final goal of detecting associations between datasets. Graphical model tools, such as the ones discussed in this project, need to be carefully evaluated in the context of genomic studies. The potential of such models to uncover new complex network patterns is tremendous and recent development in this fields has led to an to increase in their computing efficiency. This proposal has also been developed in the context of the creation of a new institute the Ludmer Centre for Neuroinformatics and Mental Health at McGill. Researchers at the new Ludmer Centre engage in a special multidisciplinary research platform, with the goal of understanding how different genetic, epigenetic and environmental factors influence brain development in children. The approach combines disciplines including neuroscience, computational biology, mathematics, genetics, epigenetics, bioinformatics, epidemiology and computer science. In this context, the proposed project is very relevant and will therefore enhance direct knowledge transfer. In particular, I expect that discoveries will open new pathways for diagnosis, prevention and treatment. **
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
RGPIN-2016-04919
Name nom
Labbe, Aurelie
Department département
Epidemiology, Biostatistics and Occupational Health
Organization id
65
Institution établissement
HEC Montréal
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2019
Competition year année de concours
2016
Award amount
22000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1508
Committee name en
Mathematics and Statistics
Committee name fr
Mathématiques et statistique
Area of application code
1101
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Human health (including medically-related psychological research)
Area of application fr
Santé publique (y compris recherche psychologique liée à la médecine)
Research subject code
3005
Research subject group en
Statistics and probability
Research subject group fr
Statistique et probabilité
Research subject en
Biostatistics
Research subject fr
Biostatistiques
Installment
inst:4 - 5
Partie
4
Nb partie
5
Application title
Component-based methods for data reduction and integration: application to genomics and neuroimaging studies.
Keyword
NSERC
Application summary
It is now well accepted that the biggest challenges and upcoming breakthroughs in the genomic field will be derived from handling big data. Not only is big data generated faster than our own capacity to fully understand, interpret and evaluate the results produced, it is also collected from an ever-increasing number of sources. As a result, statistical challenges with integrating this information into a unified model of genome function needs to be addressed. With a particular focus on genomic studies, the proposed research endeavours to develop a set of new statistical tools designed to investigate association between two or more sets of possibly high dimensional correlated variables measured on the same subjects. In particular, the general objective of the proposal is to tackle both the issue of data reduction and association analysis simultaneously, by developing statistical models achieving an optimal data reduction towards the final goal of detecting associations between datasets. Graphical model tools, such as the ones discussed in this project, need to be carefully evaluated in the context of genomic studies. The potential of such models to uncover new complex network patterns is tremendous and recent development in this fields has led to an to increase in their computing efficiency. This proposal has also been developed in the context of the creation of a new institute the Ludmer Centre for Neuroinformatics and Mental Health at McGill. Researchers at the new Ludmer Centre engage in a special multidisciplinary research platform, with the goal of understanding how different genetic, epigenetic and environmental factors influence brain development in children. The approach combines disciplines including neuroscience, computational biology, mathematics, genetics, epigenetics, bioinformatics, epidemiology and computer science. In this context, the proposed project is very relevant and will therefore enhance direct knowledge transfer. In particular, I expect that discoveries will open new pathways for diagnosis, prevention and treatment. **