Artificial intelligence for the evaluation of profiles, trajectories and management of vulnerability in health: COVID-19, frailty of the elderly and cancer
SENEC (university hospital federation)
Understanding SENEsCence to improve life course health trajectory
Underway
Collaboration type
Research structure
Main objectives
SENEC aims to develop a common multi-scale and multidisciplinary approach to chronic diseases (red blood cell, neurodegenerative, pulmonary, cardiovascular and metabolic) with a particular focus on:
- the links between the pathogenesis and progression of these diseases and the load of cellular senescence
- the trans-generational transmission of premature aging.
Based on patient cohorts, biological and imaging collections and preclinical models, SENEC aims to
- Assess the impact of cellular senescence among other confounding factors (environmental, social and genetic) in the prediction of organ damage
- Study the intergenerational inheritance of premature aging in clinical and preclinical models of aging (focus on obesity and sickle cell disease) and predict health trajectories over the life course
- Establish the framework for clinical trials of interventions targeting aging
- the links between the pathogenesis and progression of these diseases and the load of cellular senescence
- the trans-generational transmission of premature aging.
Based on patient cohorts, biological and imaging collections and preclinical models, SENEC aims to
- Assess the impact of cellular senescence among other confounding factors (environmental, social and genetic) in the prediction of organ damage
- Study the intergenerational inheritance of premature aging in clinical and preclinical models of aging (focus on obesity and sickle cell disease) and predict health trajectories over the life course
- Establish the framework for clinical trials of interventions targeting aging
Members
INSERM, INRIA, AP-HP, INRA, CNRS, ENSAM, UPEC, Université Paris Saclay, Fondation PremUp, ANSES
Partenaires privés : OPALE , MITOLOGICS , GREENPHARMA S.A.S , METABRAIN RESEARCH , SANOFI, CHIC
Other projects
Description
Learning a deep representation of patient records for event prediction and patient segmentation
Names of partners involved
AP-HP, Inria