Research Projects
D-SOLVE: THE PATH TO PERSONALISED HEPATITIS D TREATMENT
Hepatitis D is by far the most severe form of chronic viral hepatitis frequently leading to liver failure, hepatocellular carcinoma and death. However, there is very limited knowledge on disease pathophysiology and host-virus interactions explaining the large interindividual variability in the course of hepatitis D. Hannover Medical School (MHH) with its Department of Gastroenterology, Hepatology and Endocrinology and its Centre for Individualised Infection Medicine (CiiM), a joint institution of MHH and the Helmholtz Centre for Infection Research (HZI) in Braunschweig, coordinates an international project for an unbiased screening of a large multicenter cohort of well-defined HDV-infected patients to better understand individual factors determining the outcome of infection and to identify subjects benefitting from currently available treatments. In a highly competitive call for proposals, the D-SOLVE consortium (“Understanding the individual host response against Hepatitis D Virus to develop a personalized approach for the management of hepatitis D”) has received four-year funding of 6.75 million euros from the Horizon 2020 EU Horizon Call „Personalised medicine and infectious diseases: understanding the individual host response to viruses (e.g. SARS-CoV-2)“ of the European Union. In addition to the MHH as project partner and coordinator, institutions from Germany, France, Italy, Sweden and Romania are involved in the project.
Project Coordinators:
Prof Heiner Wedemeyer
Medizinische Hochschule Hannover, Germany
Funding by European Union
INTEGRATED EARLY WARNING SYSTEM FOR LOCAL RECOGNITION, PREVENTION, AND CONTROL FOR EPIDEMIC OUTBREAKS (LOKI)
Ever since the last Coronavirus epidemic caused by SARS-CoV-1, plans and tools for the containment of epidemics are being developed. However, an appropriate early warning system for local health authorities addressing this need on a regional, targeted level is not available. In the current SARS-CoV-2 pandemic, the need for such a system becomes increasingly obvious. The heterogeneity of different regions and localized outbreaks require a locally adapted monitoring and evaluation of infection dynamics. Early recognition of an emerging epidemic is a crucial part of a successful intervention. The comparison of Germany to other European nations illustrates how crucial a timely implementation of non-pharmaceutical interventions is for the containment of an epidemic. Hence, continuous monitoring of infection processes is indispensable. For strategic planning of political interventions, epidemiological modelling and scenario calculations for forecasting and evaluation of interventions and scenarios have shown their importance. The accuracy of such forecasts largely depends on the robustness and broadness of the underlying data. Further, there is a need for an intelligible presentation of often complex simulation results without oversimplification of their interpretation and inherent uncertainty. In this proposal, we develop a platform that integrates data streams from various sources in a privacy preserving manner. For their analysis, a variety of methods from machine learning to epidemiological modeling are employed to detect local outbreaks early on and enable an evaluation for different assumptions and on different scales. These models will be integrated into automatized workflows and presented in an interactive web application with custom scenario simulations. The platform will be based on insights gained by retrospective and prospective evaluation of the COVID-19 pandemic, using SARS-CoV-2 as a blueprint for the prevention and containment of future respiratory virus epidemics. The platform will be transferred to the Academy for Public Health Services and optimized in pilot projects with selected local health authorities under real-world conditions.
Project Coordinators:
Prof. Dr. Cas Cremers
CISPA Helmholtz Center for Information Security
Prof. Dr. Mario Fritz
CISPA Helmholtz Center for Information Security
Funding by Helmholtz Research for grand challenges
CRUSHING ANTIMICROBIAL RESISTANCE USING EXPLAINABLE AI (AMR-XAI)
Antimicrobial Resistance (AMR) is perhaps the most urgent threat to human health. Since their discovery over a century ago, antibiotics have greatly improved human life expectancy and quality: many diseases went from life-threatening to mild inconveniences. Miss- and over-usage of these drugs, however, has caused microbes to develop resistance to even the most advanced drugs; diseases once considered conquered are becoming devastating again. While individual resistance mutations are well-researched, knowing which new mutations can cause antimicrobial resistance is key to developing drugs that reliably sidestep microbial defenses. In this project we propose to gain this knowledge via explainable artificial intelligence, by developing and applying novel methods for discovering easily interpretable local patterns that are significant with regard to one or multiple classes of resistance. That is, we propose to learn a small set of easily interpretable models that together explain the resistance mechanisms in the data, using statistically robust methods for discovering significant subgroups, as well as information theoretic approaches to discovering succinct sets of noise-robust rules. Key to our success will be the tight integration of domain expertise into the development of the new algorithms, early evaluation on real-world data, and the potential available in the host institute to evaluate particularly promising results in the lab.
Project Coordinators:
Jilles Vreeken
CISPA Helmholtz Center for Information Security
Funding by Helmholtz Research for grand challenges
Trustworthy Federated Data Analytics (TFDA)
To solve future grand challenges, data, computational power and analytics expertise need to be brought together at unprecedented scale. The need for data has become even larger in the context of recent advances in machine learning. Therefore, data-centric digital systems commonly exhibit a strong tendency towards centralized structures. While data centralization can greatly facilitate analysis, it also comes with several intrinsic disadvantages and threats not only from a technical but more importantly also from a legal, political and ethical perspective. Rooting in sophisticated security or trust requirements, overcoming these issues is cumbersome and time consuming. As a consequence, many research projects are substantially hindered, fail or are simply not addressed. In this interdisciplinary project we aim at facilitating the implementation of decentralized, cooperative data analytics architectures within and beyond Helmholtz by addressing the most relevant issues in such scenarios. Trustworthy Federated Data Analytics (TFDA) will facilitate bringing the algorithms to the data in a trustworthy and regulatory compliant way instead of going a data-centric way. TFDA will address the technical, methodical and legal aspects when ensuring trustworthiness of analysis and transparency regarding the analysis in- and outputs without violating privacy constraints. To demonstrate applicability and to ensure the adaptability of the methodological concepts, we will validate our developments for the usage in medical research with the use case “Federated radiation therapy study” before disseminating the results.
Project Coordinators:
Prof. Dr. Mario Fritz
CISPA Helmholtz Center for Information Security
Dr. Ralf Floca
German Cancer Research Center (DKFZ)
Funding by the Helmholtz Association through the Initiative and Networking Fund
Protecting Genetic Data with Synthetic Cohorts from Deep Generative Models (PRO-GENE-GEN)
Genetic data is highly privacy sensitive information and therefore is protected under stringent legal regulations, making sharing it burdensome. However, leveraging genetic information bears great potential in diagnosis and treatment of diseases and is essential for personalized medicine to become a reality. While privacy preserving mechanisms have been introduced, they either pose significant overheads or fail to fully protect the privacy of sensitive patient data. This reduces the ability to share data with the research community which hinders scientific discovery as well as reproducibility of results. Hence, we propose a different approach using synthetic data sets that share the properties of patient data sets while respecting the privacy. We achieve this by leveraging the latest advances in generative modeling to synthesize virtual cohorts. Such synthetic data can be analyzed with established tool chains, repeated access does not affect the privacy budget and can even be shared openly with the research community. While generative modeling of high dimensional data like genetic data has been prohibitive, latest developments in deep generative models have shown a series of success stories on a wide range of domains. The project will provide tools for generative modeling of genetic data as well as insights into the long-term perspective of this technology to address open domain problems. The approaches will be validated against existing analysis that are not privacy preserving. We will closely collaborate with the scientific community and propose guidelines how to deploy and experiment with approaches that are practical in the overall process of scientific discovery. This unique project will be the first to allow the generation of synthetic high-dimensional genomic information to boost privacy compliant data sharing in the medical community.
Project Coordinators:
Dr. Matthias Becker
German Research Center for Neurodegenerative Diseases (DZNE)
Prof. Dr. Mario Fritz
CISPA Helmholtz Center for Information Security
Funding by the Helmholtz Association through the Initiative and Networking Fund