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.

Artificial intelligence technology, modified

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.

Doctor using ultrasound equipment screening of pregnant woman.

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…

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A new artificial intelligence (AI) method called BioPathNet helps researchers systematically search large biological data networks for hidden connections – from gene functions and disease mechanisms to potential therapeutic approaches. BioPathNet was…

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With single-cell technologies, researchers can measure millions of cells in parallel and track how each one responds to specific perturbations – targeted interventions such as drug treatments, changes in gene activity or disease-like conditions.…

Researchers have conducted the most comprehensive analysis to date linking plasma proteins to genetic variation in individuals from continental Africa. Their work addresses a long-standing gap by studying a population grossly underrepresented in…

Researchers from Helmholtz Munich, the Technical University of Munich (TUM), and international partner institutions have generated one of the most detailed cellular maps of carotid artery plaques to date. Their work shows how different cell types and…

The European Commission has appointed Prof. Eleftheria Zeggini, Director of the Institute of Translational Genomics at Helmholtz Munich, to the Scientific Council of the European Research Council (ERC). Alongside five newly selected members, she will…

19.02.2026

Host: Iva Pritisanac
Title: “Condensation of satellite DNA by the disordered protein D1 safeguards nuclear mechanostability”
Time: 11.00 (CET)
Location: Hybrid

26.02.2026

Host: Carsten Marr
Title: "Digital pathology and single cell genomic identify monocyte-derived interstitial macrophages as drivers of lung allograft rejection”
Time: 16.00 (CET)
Location: Virtual

19.03.2026

Host: Ele Zeggini
Title: TBD
Time: 11.00 (CET)
Location: Hybrid

16.04.2026

Host: Ele Zeggini
Title: TBD
Time: 11.00 (CET)
Location: Hybrid

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

Yépez, V.A. ; Demidov, G. ; Ellwanger, K. ; Laurie, S. ; Luknárová, R. ; Joseph Maran, M.I. ; Hentrich, T. ; Sagath, L. ; van der Sanden, B. ; Astuti, G. ; Neveling, K. ; Batlle-Masó, L. ; Beijer, D. ; Brechtmann, F. ; Caballero-Oteyza, A. ; Dabad, M. ; Denommé-Pichon, A.S. ; Doornbos, C. ; Eddafir, Z. ; Estévez-Arias, B. ; Kilicarslan, O.A. ; Kolen, I.H.M. ; Krass, L. ; Lohmann, K. ; Londhe, S. ; López-Martín, E. ; Maassen, K. ; Macken, W. ; Martínez-Delgado, B. ; Mei, D. ; Mertes, C. ; Minardi, R. ; Morsy, H. ; Mueller, J.S. ; Natera-de Benito, D. ; Nelson, I. ; Oud, M.M. ; Paramonov, I. ; Picó, D. ; Piscia, D. ; Polavarapu, K. ; Raineri, E. ; Savarese, M. ; Smal, N. ; Steehouwer, M. ; Steyaert, W. ; Swertz, M.A. ; Thomsen, M. ; Töpf, A. ; Van de Vondel, L. ; van der Vries, G. ; Vitobello, A. ; Wilke, C. ; Zurek, B. ; T' Hoen, P.B. ; Matalonga, L. ; Vissers, L.E.L.M. ; Gilissen, C. ; Schulze-Hentrich, J. ; Beltran, S. ; Esteve-Codina, A. ; Hoischen, A. ; Gagneur, J. ; Graessner, H.

Author Correction: The Solve-RD Solvathons as a pan-European interdisciplinary collaboration to diagnose patients with rare disease.

Yang, H. ; Narayan, S. ; Bordes, J. ; van Doeselaar, L. ; De Donno, C. ; Eder, M. ; Menegaz, D. ; Huettl, R.E. ; Brix, L.M. ; Mitra, S. ; Springer, M. ; Müller, M.B. ; Chen, A. ; Deussing, J.M. ; Lopez, J.P. ; Schmidt, M.V.

Mineralocorticoid receptor in glutamatergic neurons modulates anxiety exclusively in male mice via regulation of the actin-bundling factor Fam107a.

Moutakanni, T. ; Couprie, C. ; Yi, S. ; Doron, M. ; Chen, Z.S. ; Moshkov, N. ; Gardes, E. ; Caron, M. ; Touvron, H. ; Joulin, A. ; Bojanowski, P. ; Pernice, W.M. ; Caicedo, J.C.

Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy.

Nakamura, K. ; Kishita, Y. ; Sugiura, A. ; Ozaki, K. ; Yatsuka, Y. ; Matsumoto, N. ; Okazaki, A. ; Prokisch, H. ; Maruyama, K. ; Iwasa, H. ; Murayama, K. ; Matsumoto, H. ; Ohtake, A. ; Shiraishi, Y. ; Okazaki, Y.

Identification of intronic variants in NDUFA3 as a cause of leigh syndrome by whole genome sequencing and RNA sequencing.

Eichhorn, H. ; Spieker, V. ; Hammernik, K. ; Saks, E. ; Felsner, L. ; Weiss, K. ; Preibisch, C. ; Schnabel, J.A.

Motion-robust T∗2 quantification from low-resolution gradient echo brain MRI with physics-informed deep learning.

Yu, Z. ; Zhang, S. ; Qiao, N. ; Zhao, Y. ; Yu, L. ; Peng, T. ; Zhang, X.Y.

FM2: Fusing multiple foundation models for pathology image analysis via disentangled consensus-divergence representation.

Yao,Y. ; Wolf, K. ; Breitner-Busch, S. ; Zhang, S. ; Waldenberger, M. ; Winkelmann, J. ; Schneider, A.E. ; Peters, A.

Long-term exposure to traffic-related air pollution is associated with epigenetic age acceleration.

Niessen, N. ; Pirkl, C.M. ; Solana, A.B. ; Eichhorn, H. ; Spieker, V. ; Huang, W. ; Sprenger, T. ; Menzel, M.I. ; Schnabel, J.A.

INR meets multi-contrast MRI reconstruction.

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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