Aims of this collaborative project

FAIrPaCT I

The goal of this consortium, consisting of the University Medical Center Göttingen, the University Hospital Giessen and Marburg and the Technical University Munich, is to develop a software
system supported by federated artificial intelligence called FAIrPaCT that will enable the analysis of clinical patient data and molecular cancer cell data from patients with pancreatic cancer across
institutes. Pancreatic cancer is a highly aggressive malignancy with a rising incidence, predicted to become the second leading cause of cancer-related death by 2030 in the industrialised world. Due
to its extraordinarily aggressive, locally invasive tumour biology with a tendency to distant metastases, and the exceptionally high and heterogeneous resistance to conventional
chemotherapy, therapy is often difficult.
Our project combines three of the largest patient cohorts (KFO5002, KFO325, SFB1321) on pancreatic cancer in Germany, which are unique in size and heterogeneity. In combination with innovatively tailored federated artificial intelligence methods, we are able to train robust high-performance models to estimate the probability of success for specific treatment approaches. Moreover, the FAIrPaCT framework will enable the identification of important parameters that drive treatment response, so called markers. These can provide key details about the molecular mechanisms that influence therapy success and thus can support the development of improved drugs and enable personalised treatment strategies. To address the needs of stakeholders such as medical and computational researchers, and patient communities all software packages will be open-source, data will be FAIR, and results will be published. Finally, moving towards cross-cohort analysis enables us to benefit from heterogeneous local data, to build more robust and clinically relevant models, identify globally relevant markers and ultimately make a step further towards artificial intelligence-supported precision medicine.

For more information, please see here.

Contribution of the Department of Medical Bioinformatics

In FAIrPaCT I our department coordinates the 

Workpackage 6: Interpretation and xAI for PDAC Models
This comprises extraction of clinically relevant features from the trained in the FAI models (WP5), statistical evaluation and interpretationof those features.
We will apply and extend in-house developed methods to interpret machine learning models especially in the context of molecular networks. These approaches will
elucidate potential causes and molecular mechanisms that drive the predictive performance of classifiers in PDAC. 

In FAIrPaCT II we coordinate the Workpackages 3:

Multimodal Domain-Adaptation and xAI for PDAC Models 
WP3 will develop a multimodal domain adaptation approach to enhance our understanding and model accuracy, involving separate modality models, pairwise and triple modal domain adaptation. The adapted multimodal model in a shared domain provides insights into PDAC tumor characteristics, subtype classification, and treatment response. Cross-modality knowledge transfer improves predictions across diverse data, resulting in a robust model. Deliverables include adapted multi-domain adaptation methods, refined multimodal models, cross-modality explanations, and integrated xAI insights based on SHAP and LIME.