"Muu" projekt MMTAT12238
MMTAT12238 "European Medical Information Framework (EMIF) (1.01.2013−31.12.2017)", Jaak Vilo, Tartu Ülikool, Matemaatika-informaatikateaduskond, Arvutiteaduse instituut, Tartu Ülikool, Loodus- ja täppisteaduste valdkond, Arvutiteaduse instituut.
MMTAT12238
Euroopa Meditsiinilise Informatsiooni Raamistik
European Medical Information Framework (EMIF)
European Medical Information Framework (EMIF)
1.01.2013
31.12.2017
Teadus- ja arendusprojekt
Muu
ValdkondAlamvaldkondCERCS erialaFrascati Manual’i erialaProtsent
4. Loodusteadused ja tehnika4.6. ArvutiteadusedT120 Süsteemitehnoloogia, arvutitehnoloogia1.1. Matemaatika ja arvutiteadus (matemaatika ja teised sellega seotud teadused: arvutiteadus ja sellega seotud teadused (ainult tarkvaraarendus, riistvara arendus kuulub tehnikavaldkonda)90,0
3. Terviseuuringud3.11. Terviseuuringutega seotud uuringud, näiteks biokeemia, geneetika, mikrobioloogia, biotehnoloogia, molekulaarbioloogia, rakubioloogia, biofüüsika ja bioinformaatikaB110 Bioinformaatika, meditsiiniinformaatika, biomatemaatika, biomeetrika 3.1. Biomeditsiin (anatoomia, tsütoloogia, füsioloogia, geneetika, farmaatsia, farmakoloogia, kliiniline keemia, kliiniline mikrobioloogia, patoloogia)10,0
AsutusRiikTüüp
Innovative Medicines Initiative Joint Undertaking
PerioodSumma
01.01.2013−31.12.2017333 835,00 EUR
333 835,00 EUR
IMI JU collaborative project

EMIF plans to address the logistical challenges of developing a sustainable and scalable information framework which has the potential to access data on a scale and at a level of detail not currently available which will completely re‐shape the way researchers currently approach key scientificquestions and also to open avenues of research that so far have been out of reach. The current project will focus on two such research questions to provide focus and some guidance for the framework development: The determination of protective and precipitating factors for conversion from pre‐dementia cognitive dysfunction to dementia syndromes as well as conversion from prodromal Alzheimer’s disease to typical or atypical Alzheimer’s disease. The identification and validation of markers that predict such conversion in order to facilitate the development of novel disease modification therapies and lead the way towards stratified selection of individuals for clinical trials. The discovery of predictors of the metabolic complications of adult and paediatric obesity, which shall lead to innovative diagnostic tests, pave the way to novel therapeutics targeted tohigh‐risk individuals, and provide the infrastructure to select individuals for such targeted pharmacological interventions. Research into the above two questions will include an extreme phenotype approach.This is a relatively new and powerful way of investigating disease and phenotypes including assessing risk factors for disease onset, progression and outcomes. The extreme phenotype approach is based on the concept that individuals at the extreme of the distribution of a particular trait have a high probability of having a mono‐ or oligogenic predisposition to this trait. Elucidation of the genetic variants which explain why a certain individual is at the extreme of the distribution is now feasible using breakthrough technologies like exome sequencing and possibly whole genome sequencing and direct comparison of the extremes. This information can then be applied to understand less extreme variations in the phenotype, including the discovery of molecular markers for diagnostic and predictive purposes and to develop innovative therapeutics for the condition under evaluation. This approach will also help identify high‐risk individuals for therapeutic intervention, both for investigational medicines as well as approved medicines. This approach depends on access to large data sources.
EMIF plans to address the logistical challenges of developing a sustainable and scalable information framework which has the potential to access data on a scale and at a level of detail not currently available which will completely re‐shape the way researchers currently approach key scientificquestions and also to open avenues of research that so far have been out of reach. The current project will focus on two such research questions to provide focus and some guidance for the framework development: The determination of protective and precipitating factors for conversion from pre‐dementia cognitive dysfunction to dementia syndromes as well as conversion from prodromal Alzheimer’s disease to typical or atypical Alzheimer’s disease. The identification and validation of markers that predict such conversion in order to facilitate the development of novel disease modification therapies and lead the way towards stratified selection of individuals for clinical trials. The discovery of predictors of the metabolic complications of adult and paediatric obesity, which shall lead to innovative diagnostic tests, pave the way to novel therapeutics targeted tohigh‐risk individuals, and provide the infrastructure to select individuals for such targeted pharmacological interventions. Research into the above two questions will include an extreme phenotype approach.This is a relatively new and powerful way of investigating disease and phenotypes including assessing risk factors for disease onset, progression and outcomes. The extreme phenotype approach is based on the concept that individuals at the extreme of the distribution of a particular trait have a high probability of having a mono‐ or oligogenic predisposition to this trait. Elucidation of the genetic variants which explain why a certain individual is at the extreme of the distribution is now feasible using breakthrough technologies like exome sequencing and possibly whole genome sequencing and direct comparison of the extremes. This information can then be applied to understand less extreme variations in the phenotype, including the discovery of molecular markers for diagnostic and predictive purposes and to develop innovative therapeutics for the condition under evaluation. This approach will also help identify high‐risk individuals for therapeutic intervention, both for investigational medicines as well as approved medicines. This approach depends on access to large data sources.
TegevusProtsent
Alusuuring100,0