====== Unraveling Omics-Data for the Identification of Long-/Post-COVID Therapeutic Targets using Artificial Intelligence (OUTCAST-AI) ======

{{ :research:projects:outcast_ai:outcast-ai-logo.png?400|}}

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** || [[about:people: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 =====

  * [[about:people:julian kunkel]]
  * [[about:people:lara hauke]]
  * [[about:people:michael bidollahkhani]]

==== Consortium ====

  * [[https://www.uni-goettingen.de/|Georg-August-Universität Göttingen]] / [[https://www.umg.eu/|Universitätsmedizin Göttingen (UMG)]]
  * [[https://www.uni-muenchen.de/|Ludwig-Maximilians-Universität München (LMU)]]
  * [[https://www.gwdg.de/|GWDG]]

===== 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 €

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