The aim of the project is to develop a decision support system that can perform drug repositioning assessments for Childhood Acute Leukemia using Digital Twin-oriented mechanisms and Deep Learning techniques. The project targets in this aim axis are determined as follows:
- Target-1: Effectively detecting drug repositioning for Childhood Acute Leukemia with Deep Learning.
- Target-2: Developing a comprehensive computational patient model that will reflect the effects of repositioned drugs on patients.
- Target-3: Creating an architecture that directs the computational patient model to Deep Learning.
- Target-4: Developing a Generative Artificial Intelligence mechanism that creates Digital Twin-oriented synthetic patients that will enable patient-drug assessments in the Childhood Acute Leukemia scope.
- Target-5: Surrounding the created drug repositioning approach with a usable and reliable decision support system structure.
- Target-6: Verifying the effectiveness and usability of the developed decision support system with evaluations.
- Target-7: Training graduate students.
- Target-8: Realizing interdisciplinary university-university cooperation.