Unraveling Omics-Data for the Identification of Long-/Post-COVID Therapeutic Targets using Artificial Intelligence (OUTCAST-AI)
OutCast AI is a BMBF-funded research project in the funding measure “Long-COVID Daten”. The project investigates Long-/Post-COVID using AI-based analysis of molecular (Omics) and clinical data to identify distinct disease subtypes and potential therapeutic targets. In parallel, the project requires secure and scalable computing and data workflows to enable compliant processing of sensitive health data across collaborating institutions.
| Contact | Dr. Julian Kunkel | ||
| Funding measure | Long-COVID Daten | ||
| Project carrier | Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), DLR-Projektträger | ||
| Applicant | Georg-August-Universität Göttingen - Universitätsmedizin Göttingen | ||
| Duration | 01.03.2025 bis 28.02.2027 | ||
| Acronym | Outcast-AI | ||
| Project topic | Analyse von Omics-Daten zur Identifizierung von therapeutischen Targets für das Long- / Post-COVID: Ein KI-basierter Ansatz |
People from HPS
Consortium
Goals
OutCast AI aims to apply state-of-the-art Machine Learning and Artificial Intelligence methods to:
- Integrate and analyze Omics, clinical, and routine data from large Long-/Post-COVID cohorts
- Differentiate and classify LC/PC subtypes based on molecular and clinical signatures
- Identify biomarkers and potential therapeutic targets for subtype-specific treatment strategies
- Enable earlier diagnosis and improved therapy planning, contributing to reduced socioeconomic impact
Funding
The project is funded within the BMBF measure “Long-COVID Daten”. The planned project duration is 01.03.2025 to 28.02.2027. The funding rate is 100%.
Key budget figures (AZAP summary):
- Gesamtmittel: 562.063,58 €
- Projektpauschale: 112.412,71 €
- Zuwendung inkl. Projektpauschale: 674.476,29 €