Research Questions:
! How can the Automated Intelligence Genetics (AIG) technology leverage UK Biobank genomic and clinical data to enhance predictive modeling in personalized medicine?
! What are the most relevant genetic and clinical biomarkers associated with aging-related conditions, particularly frailty, sarcopenia, and motor decline?
! How can multi-scale predictive models integrating genetic and clinical data improve the early detection, risk stratification, and personalized interventions for aging-related disorders?
Aims:
! To validate the AIG technology by utilizing UK Biobank’s genomic and clinical datasets to develop and optimize predictive models for aging-related conditions.
! To identify genetic risk factors and clinical markers associated with frailty, sarcopenia, and motor decline, refining their predictive value through machine learning and statistical approaches.
! To establish a robust AI-driven framework for integrating genomic and clinical data in predictive healthcare, facilitating its future clinical translation.
! Strengthen scientific collaborations with prestigious institutions and leading research groups, such as the collaborations established with renowned experts like Fernando Rodríguez Artalejo (UAM) and Andrés Moya (FISABIO), among others, in the framework of several R&D projects.
This initiative aims to foster scientific excellence by integrating knowledge from multiple disciplines and demonstrating alignment with highly competitive national and international funded R&D&I projects. In addition, the project remains open to expand collaborations with other distinguished research teams to further advance innovation in personalized medicine.