Our group focuses on problem driven development of AI/ML approaches to data exploration, hypothesis generation and computational scientific discovery to facilitate translational biomedicine.

We work on representation learning and (un-)supervised analysis of spatial omics data. We develop new and extend existing explainable, scalable and readily deployable methods for multi-view learning, graph neural networks, metaheuristic optimization and optimal transport to:

• Identify clinically relevant spatial regions and interactions by explanatory modeling and optimization of global and local tissue/condition specific persistent multicellular patterns.
• Learn higher order spatial structural and functional organization to form taxonomical models of tissues for comparative analyses and generation of in-silico samples.
• Integrate spatial multiomics data with databases of prior knowledge to discover context specific mechanistic insighs spanning multiple omics layers.

Our interest is to address questions of structure-function relationships in disease, progression and response to treatment.

We value collaborations with clinical, experimental biology groups and groups working on the development of novel methods for the acquisition of spatial omics data. We welcome synergistic collaborations with computational groups towards the construction of more robust theoretical and computational frameworks for the analysis of all aspects of biomedical data and beyond.

Group Leader

Members

Associated Members

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Publications

Ceccarelli, F., Lio, P., Saez-Rodriguez, J., Holden, S. B., Tanevski, J. Topography Aware Optimal Transport for Alignment of Spatial Omics Data. bioRxiv:2025.04.15.648894 (2025).
Schiller, C., Ibarra-Arellano, Miguel A., Bestak, K., Tanevski, J., Schapiro, D. Comparison and Optimization of Cellular Neighbor Preference Methods for Quantitative Tissue Analysis. bioRxiv:2025.03.31.646289 (2025).
Tanevski, J., Vuillard, L., Hartmann, F., Saez-Rodriguez, J. Learning tissue representation by identification of persistent local patterns in spatial omics data. bioRxiv:2024.03.06.583691 (2024).
Rahimi, A., Vale-Silva, L.A., Faelth Savitski, M., Tanevski, J., Saez-Rodriguez, J. DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics. Nature Communications 15 (2024).
Dimitrov, D., Schäfer, P.S.L., Farr, E., et al. LIANA+ provides an all-in-one framework for cell–cell communication inference. Nature Cell Biology (2024).
Laury, A. R., Zheng, S., Aho, N. et al. Opening the black box: spatial transcriptomics and the relevance of AI-detected prognostic regions in high grade serous carcinoma. Modern Pathology 100508 (2024).
Paton, V., Gabor, A., Ramirez Flores, R.O. et al. Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results. Nucleic Acids Research (2024).
Wünnemann, F., Sicklinger, F., Bestak, K., et al. Spatial omics of acute myocardial infarction reveals a novel mode of immune cell infiltration. bioRxiv:2024.05.20.594955 (2024).
Heumos, L., Schaar, A.C., Lance, C. et al. Best practices for single-cell analysis across modalities. Nature Reviews Genetics 24, 550–572 (2023).
Tanevski, J., Ramirez Flores, R.O., Gabor, A. et al. Explainable multiview framework for dissecting spatial relationships from highly multiplexed data. Genome Biology 23, 97 (2022).
Kuppe, C., Ramirez Flores, R.O., Li, Z. et al. Spatial multi-omic map of human myocardial infarction. Nature 608, 766–777 (2022).
Gabor, A., Tognetti, M., Driessen, A., Tanevski, J. et al. Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd. Molecular Systems Biology, 17(10), e10402 (2021).
Schwabenland, M., Salié, H., Tanevski, J. et al. Deep spatial profiling of human COVID-19 brains reveals neuroinflammation with distinct microanatomical microglia-T-cell interactions. Immunity 54(70), 1594-1610.e11 (2021)
Holland, C.H., Tanevski, J., Perales-Patón, J. et al. Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data. Genome Biology 21, 36 (2020).
Tanevski, J., Nguyen, T., Truong, B. et al. Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data. Life Science Alliance 3 (11), e202000867 (2020).
Tanevski, J., Todorovski, L., Džeroski, S. Combinatorial search for selecting the structure of models of dynamical systems with equation discovery. Engineering Applications of Artificial Intelligence 89, 103423 (2020).
Tanevski, J., Todorovski, L., Džeroski, S. Process-based design of dynamical biological systems. Scientific Reports 6, 34107 (2016).
Tanevski, J., Todorovski, L., Džeroski, S. Learning stochastic process-based models of dynamical systems from knowledge and data. BMC Systems Biology 10, 30 (2016).

Positions

Interns/Master theses

We continiously offer opportunities for internships and supervision of master theses. We recommend that the duration of the internships is no shorter than three months.

PhD

We currently have an open PhD position in modeling and integration of multilayered omics data with microscopy/pathology imaging data with focus on patients with heart failure with preserved ejection fraction. For more information see the post on jobRxiv.

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For all PhD and Postdoc postions we offer:

• Work contract and funding according to TV-L with all corresponding social benefits.
• Stimulating and supporting interdisciplinary research environment with access to international research networks.
• Access to state-of-the-art spatial omics and clinical data.
• Access to high performance computing infrastructure.
• Access to further training opportunities offered by the Heidelberg University and Heidelberg University Hospital.

To apply please submit a letter of motivation tailored to the position (1 page), CV and a list of references with contact details to contact<at>tanevskilab.org.