Projects

Beim Projekt Real4Reg, das vom Bundesinstitut für Arzneimittel und Medizin­produk­te (BfArM) koordiniert wird, sollen Abrechnungsdaten KI-gestützt ausgewertet werden, um bessere Erkennt­nisse zu Arzneimittelneben- und -wechselwirkungen zu generieren.

Patients with severe psychiatric disorders such as schizophrenia, bipolar disorder and clinical depression often develop resistance to drug therapy. Even when the early signs of treatment resistance (TR) are detected, patients must undergo lengthy procedures before medical professionals can prescribe adequate pharmaceutical care. The EU-funded Psych-STRATA project intends to analyse extensive clinical, genetic and biological data of psychiatric patients to establish criteria for early detection of TR. Based on the findings, it will propose treatment strategies for patients at risk of TR. The project will further create machine learning models that can predict TR risk and patient response to treatment, thus assisting medical professionals in providing more personalised treatment.

REMEDi4ALL aims to make a significant leap forward in drug repurposing. This promising approach to drug development, consisting of identifying, testing, and validating new therapeutic indications for existing medications, is a developing field but faces numerous barriers and systemic inefficiencies. Still, its potential to significantly bring down times and costs of drug development - it focuses on already approved, discontinued, shelved or investigational therapeutics - makes this novel strategy attractive for rare and neglected conditions, cancer, emerging public health threats such as COVID-19 or new drug combinations. It also translates into more sustainable health systems.

ParKInsonPredict is a joint research project of Technische Universität Dresden and Fraunhofer SCAI aiming to predict disease progression patterns in Parkinson's disease.

ADIS stands for "Early Diagnosis of Alzheimer's Disease by Immune Profiling of Cytotoxic Lymphocytes and Recording of Sleep Disturbances." The project is funded by the EU Joint Programme for Neurodegenerative Diseases Research (JPND). JPND is the largest global research initiative to address neurodegenerative disease challenges. The ADIS project (start: July 2022) will run for three years and has a budget of 1.3 million euros, of which 300,000 euros will go to Fraunhofer SCAI.

We aim to validate and commercialise biomarkers and the resulting products for affective disorders to address the many unmet needs along the entire disease trajectory.

The goal of the AIOLOS project (Artificial Intelligence Tools for Outbreak Detection and Response) is to develop a digital platform to allow for early detection of new respiratory pathogens epidemics, monitor their spread, and inform decision-makers on appropriate countermeasures. AIOLOS will provide insights for private and public decision-making in a web-based dashboard, which leverages real-time data from multiple data sources, advanced artificial intelligence (AI), and predictive modeling.

The European project DIGIPD, funded with around 1.6 million euros and coordinated by the Fraunhofer Institute for Algorithms and Scientific Computing SCAI, is investigating the extent to which digital techniques (sensors, speech recognition, recognition of facial expressions) can be used to make a more precise and individualized diagnosis and prognosis of Parkinson's disease. The project is funded by the European network for personalized medicine, ERA PerMed, in the "Joint Transnational Call 2020". The German share of DIGIPD is funded by the Federal Ministry of Education and Research (BMBF).

The zet-o-map project aims to develop better tools for the identification of teratogenic compounds. One of the most promising assays is the zebrafish embryo teratogenicity assay (ZETA). In human risk assessment the evaluation of developmental toxicity or teratogenicity requires the testing of rodents (preferably rats) and non-rodents (preferably rabbits) as described in the OECD 414 guideline. Currently human safety assessment is undergoing a paradigm shift moving towards mechanistic risk assessment and there is a high demand to replace, reduce or refine animal tests where possible for ethical and economic reasons. zet-o-map is funded by the Long Range Research Initiative (LRI) of the European Chemical Industry Council (CEFIC).

Biomedical knowledge graphs play a central role in big data integration. Bringing unstructured text into a structured, comparable format is one of the key assets. As cause and effect models, knowledge graphs can potentially facilitate clinical decision making or help to drive research towards precision medicine. Data and Knowledge Management, sometimes also called Information Management, is a core topic of Data Science. It is also a interdisciplinary field touching economics (how efficient and expensive is the solution?), psychology (do people use this solution in a way that was intended?) and of course computer science. Our aim is to build sustainable data infrastructure for biomedical data, personalized medicine, drug repurposing, reproducible AI and knowledge discovery.