Objetivo Longitudinal omics data hold great promise to improve biomarker detection and enable dynamic individualized predictions. Recent technological advances have made proteomics an increasingly attractive option but clinical longitudinal proteomic datasets are still rare and computational tools for their analysis underdeveloped. The objective of this proposal is to create a roadmap to detect clinically feasible protein markers using longitudinal data and effective computational tools. A biomedical focus is on early detection of Type 1 diabetes (T1D). Specific objectives are:1) Novel biomarker detector using longitudinal data. DynaOmics introduces novel types of multi-level dynamic markers that are undetectable in conventional single-time cross-sectional studies (e.g. within-individual changes in abundance or associations), develops optimization methods for their robust and reproducible detection within and across individuals, and validates their utility in well-defined samples. 2) Individualized disease risk prediction dynamically. DynaOmics develops dynamic individualized predictive models using the multi-level longitudinal proteome features and novel statistical and machine learning methods that have previously not been used in this context, including joint models of longitudinal and time-to-event data, and one-class classification type techniques.3) Dynamic prediction of T1D. DynaOmics builds a predictive model of dynamic T1D risk to assist early detection of the disease, which is crucial for developing future therapeutic and preventive strategies. T1D typically involves a relatively long symptom-free period before clinical diagnosis but current tools to predict early T1D risk have restricted power.The objectives involve innovative and unconventional approaches and address major unmet challenges in the field, having high potential to open new avenues for diagnosis and treatment of complex diseases and fundamentally novel insights towards precision medicine. Ámbito científico natural sciencesbiological sciencesbiochemistrybiomoleculesproteinsproteomicsmedical and health sciencesclinical medicineendocrinologydiabetesnatural sciencescomputer and information sciencescomputational sciencemedical and health scienceshealth sciencespersonalized medicinenatural sciencescomputer and information sciencesartificial intelligencemachine learning Programa(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Tema(s) ERC-StG-2015 - ERC Starting Grant Convocatoria de propuestas ERC-2015-STG Consulte otros proyectos de esta convocatoria Régimen de financiación ERC-STG - Starting Grant Institución de acogida TURUN YLIOPISTO Aportación neta de la UEn € 1 499 869,00 Dirección YLIOPISTONMAKI 20014 Turku Finlandia Ver en el mapa Región Manner-Suomi Etelä-Suomi Varsinais-Suomi Tipo de actividad Higher or Secondary Education Establishments Enlaces Contactar con la organización Opens in new window Sitio web Opens in new window Participación en los programas de I+D de la UE Opens in new window Red de colaboración de HORIZON Opens in new window Coste total € 1 499 869,00 Beneficiarios (1) Ordenar alfabéticamente Ordenar por aportación neta de la UE Ampliar todo Contraer todo TURUN YLIOPISTO Finlandia Aportación neta de la UEn € 1 499 869,00 Dirección YLIOPISTONMAKI 20014 Turku Ver en el mapa Región Manner-Suomi Etelä-Suomi Varsinais-Suomi Tipo de actividad Higher or Secondary Education Establishments Enlaces Contactar con la organización Opens in new window Sitio web Opens in new window Participación en los programas de I+D de la UE Opens in new window Red de colaboración de HORIZON Opens in new window Coste total € 1 499 869,00