Computational Health

We develop novel computational tools powered by AI to accelerate discovery and translation. We apply cutting-edge computational methods to promote personalised health. Collaboratively, we develop predictive algorithms as well as mechanistic models to analyse molecular, imaging, and clinical data of human health and disease. We thus help to create innovative diagnostics and novel treatments for environmentally triggered diseases.

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We develop robust methods for analyzing big data to address key biomedical challenges and consolidate analytical approaches using innovative digital methods. In addition, we develop novel statistical methods for trans- ethnic meta-analysis, testing for pleiotropy, rare variant burden, testing, and polygenic risk score construction.

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We develop and translate AI technologies for biomedical problems by constructing deep-learning methods and combining them with more mechanistic modeling approaches. In addition, we steer the computational aspects of developing single-cell atlases in healthy and disease state to build AI-driven analytics platforms for multimodal data, in particular from genomics and diverse imaging modalities.

We develop novel computational methods for multiomic data integration of epigenomic, transcriptomic, proteomic and genetic data and advanced phenotypic/in vivo observations. In addition, we design novel approaches for efficient data combination across omics levels, maximizing the information yield across the multidimensional space of datar from genomics and diverse imaging modalities.

A Journey into the Secrets of Human Cells

What the Human Cell Atlas (HCA) will tell us about ourselves and how scientists at Helmholtz Munich are facilitating this revolutionizing global endeavor.

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Using AI in Medicine to Combat Diseases

Artificial Intelligence (AI) is becoming increasingly crucial in medical research, and Helmholtz Munich is one of the world's leading institutions in this field. It can detect tumors as effectively as severe eye diseases.

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Big Data at Your Bedside

Analysis of genes can help in detecting diseases at an early stage as well as in determining new drug targets for therapy. Thanks to extremely powerful data processing, the field holds great promise for advancement. Helmholtz Munich is particularly at the cutting edge in the use of these new methods.

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In the future, blood samples from patients can be evaluated not only by specialists, but also by artificial intelligence. A team from Helmholtz Munich is laying the foundations for this.

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How AI is Revolutionizing Medicine

Computer systems can already independently analyze health data, learn from it and even derive therapy recommendations. Which opportunities this means for patients and what research needs to apply artificial intelligence in practice.

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Helmholtz Munich Expert Hour at German Bundestag

How can scientific breakthroughs reach the people more quickly? This question was the focus of the Helmholtz Munich Expert Hour on March 25 in the German Bundestag. Under the title “From Research to Patients – Translation and AI in Medicine”,…

12.07.2023, Neuherberg: Helmholtz Pioneer Campus Opening. Foto: Matthias Balk/Helmholtz Zentrum München

At its most recent plenary session, the Bavarian Academy of Sciences and Humanities (BAdW) elected 13 distinguished scholars as new members. Among them are three researchers from Helmholtz Munich: Prof. Maria-Elena Torres-Padilla, Prof. Fabian Theis,…

Fabian Theis bei Eröffnung des Google AI Centers Berlin

On March 5, Google opened its first German AI Center in Berlin. The event was attended by representatives from politics, business, and science, including Federal Research Minister Dorothee Bär, Federal Digital Minister Karsten Wildberger, Berlin's…

Helmholtz Munich and Novartis Biomedical Research are launching a four-year collaboration to advance kidney disease research. Starting in early 2026, the partnership will work to develop an integrated, cross-species “kidney disease atlas” at…

Dr. Arcangela Iuso from the Institute of Neurogenomics at Helmholtz Munich was awarded the Eva Luise Köhler Research Award. The foundation thus honors a highly innovative research project on RNA base editing for a rare neurodegenerative disease.

Numerous genetic studies have identified many risk variants for type 2 diabetes (T2D) – but which genes and proteins are actually involved in the disease mechanisms? An international team led by Helmholtz Munich has now used globally collected…

23.04.2026

Host: Annalisa Marsico
Title: TBD
Time: 16.00 (CEST)
Location: Virtual

30.04.2026

Host: Sebastian Lobentanzer
Title: “From Electronic Patient Records to EHDS - A long Journey Through the German Perspective”
Time: 16.00 (CEST)
Location: Virtual

30.04.2026

Host: Tingying Peng
Title: TBD
Time: 11.00 (CEST)
Location: Hybrid

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Follow the link to find the latest contributions from Computational Health Center researchers at international AI conferences:

Postdoctoral Health AI Fellows Program

The newly established Postdoctoral Health AI Fellows (HAIF) Program is dedicated to fostering independent, high-impact research in computational biomedicine and health-related AI. Our mission is to empower early-career postdoctoral scientists from computational, mathematical, engineering, and physical science disciplines by granting them early scientific independence to boldly address key challenges in computational biomedicine.

Recent advances in computation have transformed our understanding of molecular processes and their effects on health and disease. Breakthroughs in modeling, AI, and large-scale data analysis have reshaped biomedicine, enabling researchers to extract meaningful insights from increasingly complex biological datasets. As high-throughput technologies generate vast volumes of data, new challenges emerge in scale, management, analysis, and interpretation. Yet, these same developments open unprecedented opportunities to uncover complex biological mechanisms, predict system behavior, and understand pathophysiological processes.

Unlike traditional postdoctoral positions, HAIF fellows join the Computational Health Center (CHC) at Helmholtz Munich as independent early-career researchers with greater autonomy. Each fellow has access to mentoring by two senior researchers, either two CHC Principal Investigators, or one Principal Investigator and one external expert from university, industry or a start-up. With this support structure, fellows have the freedom to develop and apply comprehensive computational strategies to tackle challenges in health research.

By leveraging experimental, computational, and statistical approaches, the HAIF Program empowers fellows to explore new scientific frontiers that integrate data-driven and experimental methodologies. Working closely with experimental partners in Munich and beyond, fellows bring fresh perspectives to significant biological and biomedical challenges—stimulating innovation and driving the development of transformative solutions.

The call for applications will open in mid-December!

Apply here

Kazeminia, S. ; Marr, C. ; Rieck, B.

Topological Inductive Bias fosters Multiple Instance Learning in Data-Scarce Scenarios.

Meghdadi, M. ; Duff, J. ; Demberg, V.

Integrating language model embeddings into the ACT-R cognitive modeling framework.

Schranner, D. ; Wackerhage, H. ; Weinisch, P. ; Schlegel, J. ; Bremer, S. ; Scherr, J. ; Römisch-Margl, W. ; Riermeier, A. ; Zelger, O. ; Stöcker, F. ; Artati, A. ; Witting, M. ; Krumsiek, J. ; Halle, M. ; Schönfelder, M. ; Kastenmüller, G.

Characterizing human oxidative, anabolic and glycolytic metabolism in athletes with extreme physiologies.

Mueller, S. ; de Andrade Krätzig, N. ; Tschurtschenthaler, M. ; Silva, M.G. ; Thordsen, C. ; Trozzo, R. ; Simon, P. ; Saab, F. ; Kaltenbacher, T. ; Zukowska, M. ; Lucarelli, D. ; Öllinger, R. ; Griger, J. ; Groß, N. ; Groll, T. ; Löprich, J. ; Zaurito, A.E. ; Schömig, L.R. ; Bugter, J.M. ; Bärthel, S. ; Falcomatà, C. ; Strong, A. ; Brandt, C. ; Najajreh, M. ; Papargyriou, A. ; Maresch, R. ; Collins, K.A.N. ; Sailer, D. ; Schneeweis, C. ; Burger, S. ; Fröhlich, L.M. ; Klement, C. ; Belka, A. ; Montero, J.J. ; Jungwirth, U. ; Reichert, M. ; Moser, M. ; Neumann, J. ; Vassiliou, G. ; Cadiñanos, J. ; Varela, I. ; Marr, C. ; Alonso, D.F. ; Lollini, P.L. ; Zhao, J. ; Chesler, L. ; Isacke, C.M. ; Riedel, A. ; Braun, C.J. ; Sos, M.L. ; Beleggia, F. ; Reinhardt, H.C. ; Musteanu, M. ; Barbacid, M. ; Quante, M. ; Schmidt-Supprian, M. ; Schneider, G. ; Clare, S. ; Lawley, T.D. ; Dougan, G. ; Steiger, K. ; Conte, N. ; Bradley, A. ; Rad, L. ; Saur, D. ; Rad, R.

A disease model resource reveals core principles of tissue-specific cancer evolution.

Krauss, D. ; Richer, R. ; Albrecht, N. ; Jukic, J. ; Krebber, C.H. ; Zwiessele, P. ; German, A. ; Koelpin, A. ; Regensburger, M. ; Winkler, J. ; Eskofier, B.M.

Contactless sleep staging with radar: A transfer learning approach.

Bader, J.M. ; Makarov, C. ; Richter, S. ; Strauss, M.T. ; Held, F. ; Wahle, M. ; Lorenz, M.B. ; Pöschl, L. ; Skowronek, P. ; Thielert, M. ; Berthele, A. ; Zeng, W.F. ; Ammar, C. ; Bludau, I. ; Schubert, B. ; Theis, F.J. ; Gasperi, C. ; Hemmer, B. ; Mann, M.

Large-scale proteomics across neurological disorders uncovers biomarker panel and targets in multiple sclerosis.

Richter, T. ; Wang, W. ; Palma, A. ; Theis, F.J.

Generative models of cell dynamics: From Neural ODEs to flow matching.

Horváth, V.B. ; Tsiakas, K. ; Iascone, M. ; Choukair, D. ; Hammann, N. ; Niesert, M. ; Fichtner, A. ; Prokisch, H. ; Santer, R. ; Wagner, M. ; Hoffmann, G.F. ; Weis, D. ; Lenz, D.

Transaldolase deficiency – natural disease course towards adulthood.

Foundation Models in Health Research

Foundation models are advanced artificial intelligence systems designed to process vast amounts of data and to complete a range of downstream tasks. Being trained on extensive datasets these models are now revolutionizing biomedical research, accelerating discovery and translation. At the Computational Health Center we and our partners leverage these cutting-edge models to decode the complexities of human biology, aiming to pave the way for innovative treatments and a deeper understanding of health and disease.

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