Ongoing
CancerScan project
CancerScan addresses the lack of tools that simulate a patient’s tumoral response to specific drugs based on unique cellular and molecular makeup by developing an innovative digital pathology slide scanner capable of generating patient-specific tumour digital twins. These twins integrate multi-omics data and histopathology to model tumour behaviour and simulate treatment effects. CancerScan combines expertise in biology, AI, and hardware engineering to capture tumour communication patterns, and translate them into personalised simulations.
Past
KATY project
The main goal of the project is the creation of an AI-empowered personalized cancer therapy system that provides human-interpretable knowledge that healthcare professionals can understand, trust, and effectively use in their everyday working routine.
Combining Knowledge Graphs and Deep Learning for data mining tasks
Master Thesis Project. Development and systematic evaluation of neural approaches that explore semantic information on PPI data to solve PPI-based prediction tasks by including an ontological layer in the PPI graph.