Center for Computational and Theoretical Biology

BioMedical Data Science

Through technological advances (e.g. DNA sequencing, new imaging technologies, ...) the life sciences have turned into a data heavy science. There is huge potential to discover novel insights and address big challenges of our society using these data.

Our research focuses on development and application of (bio)informatic methods and tools to gain insights from complex data sources. We apply these methods in two main areas: Molecular Ecology and Bio-Medical Imaging. At first glance, these topics seem to have nothing in common. But in both these fields large amounts of diverse data (images, text, DNA, quantitative measurements, ...) are available, and we are not yet able to take full advantage of this rich pile of information. In both fields, there are two major challenges:

  • Every data type only holds a fraction of the available information (like pieces of a puzzle). It is necessary to properly combine all data sources to gain new insights that can be used to recommend actions or improve diagnoses and therapies. Thus, one key research area of our group is multimodal methods.
  • In both fields, there is a so-called implementation gap. That means, that new methods (like machine learning and artificial intelligence) have been shown to have impressive performance but are still not used in practice. Reasons for this gap include a lack of explainability and interpretability of how these black-box models work. Further concerns are an apparent lack of generalizability, transparency and fairness. Thus, the focus of our group is on bridging this gap.

Thesis projects

Join our group for an internship, or for your Bachelor or Master thesis.

Some projects we currently offer:

  • Developing and evaluating an image analysis pipeline for in-situ sequencing. In collaboration with Prof. Redmond Smyth.
  • Improving a deep learning model to predict prostate cancer relapse from PET/CT images. In collaboration with Dr. Wiebke Schlötelburg.
  • Dose-optimization of photon-counting CT through denoising. In collaboration with Prof. Tobias Wech
  • Multiple project ideas in collaboration with the Department of Neuroradiology and Dr. Magnus Schindehütte.
  • If you have an idea that fits the research of our group, we are very happy to discuss it.

Are you fascinated by the possibilities of modern computational methods like machine learning and artificial intelligence? Do you want to develop and apply these methods to get insights from biological or medical data? Then join our team to train and apply your skills on an interesting project in the field of BioMedical Data Science.

As long as you are motivated and willing to learn, prior knowledge is not required.

Get in touch with Markus Ankenbrand to learn more.



  • Mike Klaus
  • Joél Schaust
  • Marko Korb
  • Robin Müller
  • Gloria Castañeda Agredo


2024[ to top ]
  • Semi-automated sequence curation for reliable reference datasets in ITS2 vascular plant DNA (meta-)barcoding. Quaresma, Andreia; Ankenbrand, Markus J.; Garcia, Carlos Ariel Yadr{ó}; Rufino, Jos{é}; Honrado, M{ó}nica; Amaral, Joana; Brodschneider, Robert; Brusbardis, Valters; Gratzer, Kristina; Hatjina, Fani; Kilpinen, Ole; Pietropaoli, Marco; Roessink, Ivo; van der Steen, Jozef; Vejsn{\ae}s, Flemming; Pinto, M. Alice; Keller, Alexander. In Scientific Data, 11(1), bl 129. 2024.
  • Cardiac function in a large animal model of myocardial infarction at 7 T: deep learning based automatic segmentation increases reproducibility. Kollmann, Alena; Lohr, David; Ankenbrand, Markus J.; Bille, Maya; Terekhov, Maxim; Hock, Michael; Elabyad, Ibrahim; Baltes, Steffen; Reiter, Theresa; Schnitter, Florian; Bauer, Wolfgang R.; Hofmann, Ulrich; Schreiber, Laura M. In Scientific Reports, 14(1), bl 11009. 2024.
2023[ to top ]
  • Ten (mostly) simple rules to future-proof trait data in ecological and evolutionary sciences. Keller, Alexander; Ankenbrand, Markus J.; Bruelheide, Helge; Dekeyzer, Stefanie; Enquist, Brian J.; Erfanian, Mohammad Bagher; Falster, Daniel S.; Gallagher, Rachael V.; Hammock, Jennifer; Kattge, Jens; Leonhardt, Sara D.; Madin, Joshua S.; Maitner, Brian; Neyret, Margot; Onstein, Renske E.; Pearse, William D.; Poelen, Jorrit H.; Salguero-Gomez, Roberto; Schneider, Florian D.; Tóth, Anikó B.; Penone, Caterina. In Methods in Ecology and Evolution, 14(2), K. Bacon (red.), bll 444–458. 2023.
  • MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images. Li, Lei; Wu, Fuping; Wang, Sihan; Luo, Xinzhe; Martín-Isla, Carlos; Zhai, Shuwei; Zhang, Jianpeng; Liu, Yanfei; Zhang, Zhen; Ankenbrand, Markus J.; Jiang, Haochuan; Zhang, Xiaoran; Wang, Linhong; Arega, Tewodros Weldebirhan; Altunok, Elif; Zhao, Zhou; Li, Feiyan; Ma, Jun; Yang, Xiaoping; Puybareau, Elodie; Oksuz, Ilkay; Bricq, Stephanie; Li, Weisheng; Punithakumar, Kumaradevan; Tsaftaris, Sotirios A.; Schreiber, Laura M.; Yang, Mingjing; Liu, Guocai; Xia, Yong; Wang, Guotai; Escalera, Sergio; Zhuang, Xiahai. In Medical Image Analysis, bl 102808. 2023.
  • Recognition and reconstruction of cell differentiation patterns with deep learning. Dirk, Robin; Fischer, Jonas L.; Schardt, Simon; Ankenbrand, Markus J.; Fischer, Sabine C. In PLOS Computational Biology, 19(10), bll 1–29. Public Library of Science, 2023.
  • {Opportunistic Bacteria of Grapevine Crown Galls Are Equipped with the Genomic Repertoire for Opine Utilization}. Faist, Hanna; Ankenbrand, Markus J; Sickel, Wiebke; Hentschel, Ute; Keller, Alexander; Deeken, Rosalia. In Genome Biology and Evolution, 15(12), bl evad228. 2023.
  • Novel integrative elements and genomic plasticity in ocean ecosystems. Hackl, Thomas; Laurenceau, Raphaël; Ankenbrand, Markus J.; Bliem, Christina; Cariani, Zev; Thomas, Elaina; Dooley, Keven D.; Arellano, Aldo A.; Hogle, Shane L.; Berube, Paul; Leventhal, Gabriel E.; Luo, Elaine; Eppley, John M.; Zayed, Ahmed A.; Beaulaurier, John; Stepanauskas, Ramunas; Sullivan, Matthew B.; DeLong, Edward F.; Biller, Steven J.; Chisholm, Sallie W. In Cell, 186(1), bll 47–62.e16. Elsevier, 2023.
2022[ to top ]
  • Efficient permutation-based genome-wide association studies for normal and skewed phenotypic distributions. John, Maura; Ankenbrand, Markus J; Artmann, Carolin; Freudenthal, Jan A; Korte, Arthur; Grimm, Dominik G. In Bioinformatics, 38(Supplement\_2), bll ii5-ii12. 2022.
  • In Vitro Rearing Changes Social Task Performance and Physiology in Honeybees. Schilcher, Felix; Hilsmann, Lioba; Rauscher, Lisa; Değirmenci, Laura; Krischke, Markus; Krischke, Beate; Ankenbrand, Markus; Rutschmann, Benjamin; Mueller, Martin J.; Steffan-Dewenter, Ingolf; Scheiner, Ricarda. In Insects, 13(1). 2022.
  • A data-driven semantic segmentation model for direct cardiac functional analysis based on undersampled radial MR cine series. Wech, Tobias; Ankenbrand, Markus Johannes; Bley, Thorsten Alexander; Heidenreich, Julius Frederik. In Magnetic Resonance in Medicine, 87(2), bll 972–983. 2022.
2021[ to top ]
  • On the way to routine cardiac MRI at 7 Tesla - a pilot study on consecutive 84 examinations. Reiter, Theresa; Lohr, David; Hock, Michael; Ankenbrand, Markus Johannes; Stefanescu, Maria Roxana; Kosmala, Aleksander; Kaspar, Mathias; Juchem, Christoph; Terekhov, Maxim; Schreiber, Laura Maria. In PLOS ONE, 16(7), bll 1–18. Public Library of Science, 2021.
  • Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac {MRI}. Ankenbrand, Markus J.; Shainberg, Liliia; Hock, Michael; Lohr, David; Schreiber, Laura M. In BMC Medical Imaging, 21(1), bl 27. 2021.
  • Open Science principles for accelerating trait-based science across the Tree of Life. Gallagher, Rachael; Falster, Daniel S.; Maitner, Brian; Enquist, Brian; Ankenbrand, Markus; Balk, Meghan; Bland, Lucie; Boyle, Brad; Bravo, Catherine; Cavazos, Brittany; Fadrique, Belen; Feng, Xiao; Halbritter, Aud; Hammock, Jennifer; Hogan, James Aaron; Iversen, Colleen; Jochum, Malte; Kattge, Jens; Keller, Alexander; Madin, Joshua; Manning, Peter; McCormack, Luke; Michaletz, Sean; Park, Daniel; Pearse, William; Penone, Caterina; Perez, Timothy; Pineda-Munoz, Silvia; Poelen, Joritt; Ray, Courtenay; Salguero-Gomez, Roberto; Sauquet, Herve; Schneider, Florian; Spasojevic, Marko J.; Vandvik, Vigdis; Violle, Cyrille; Weiss, Katherine. In Nature Ecology \& Evolution, bll 294–303. Nature Publishing Group, 2021.
  • Deep learning-based cardiac cine segmentation: Transfer learning application to 7T ultrahigh-field MRI. Ankenbrand, Markus Johannes; Lohr, David; Schlötelburg, Wiebke; Reiter, Theresa; Wech, Tobias; Schreiber, Laura Maria. In Magnetic Resonance in Medicine, 86(4), bll 2179–2191. 2021.
  • Dealing with software complexity in individual-based models. Vedder, Daniel; Ankenbrand, Markus; Cabral, Juliano. In Methods in Ecology and Evolution, 12, bll 2324–2333. 2021.
  • Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction. Heidenreich, Julius F.; Gassenmaier, Tobias; Ankenbrand, Markus J.; Bley, Thorsten A.; Wech, Tobias. In European Journal of Radiology, 141, bl 109817. 2021.
  • B0 shimming of the human heart at 7T. Hock, Michael; Terekhov, Maxim; Stefanescu, Maria Roxana; Lohr, David; Herz, Stefan; Reiter, Theresa; Ankenbrand, Markus; Kosmala, Aleksander; Gassenmaier, Tobias; Juchem, Christoph; Schreiber, Laura Maria. In Magnetic Resonance in Medicine, 85(1), bll 182–196. 2021.
2020[ to top ]
  • Exploring Ensemble Applications for Multi-sequence Myocardial Pathology Segmentation. Ankenbrand, Markus J.; Lohr, David; Schreiber, Laura M. In Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images, X. Zhuang, L. Li (reds.), bll 60–67. Springer International Publishing, Cham, 2020.
  • Chronic exposure to the pesticide flupyradifurone can lead to premature onset of foraging in honeybees (Apis mellifera). Hesselbach, Hannah; Seeger, Johannes; Schilcher, Felix; Ankenbrand, Markus; Scheiner, Ricarda. In Journal of Applied Ecology, 57, bll 609–618. 2020.
  • BCdatabaser: on-the-fly reference database creation for DNA (meta-)barcoding. Keller, Alexander; Hohlfeld, Sonja; Kolter, Andreas; Schultz, Jörg; Gemeinholzer, Birgit; Ankenbrand, Markus J. In Bioinformatics, 36(8), bll 2630–2631. 2020.
  • A systematic comparison of chloroplast genome assembly tools. Freudenthal, Jan A.; Pfaff, Simon; Terhoeven, Niklas; Korte, Arthur; Ankenbrand, Markus J.; F{ö}rster, Frank. In Genome Biology, 21(1), bl 254. 2020.
  • {{G}enomes of the {V}enus {F}lytrap and {C}lose {R}elatives {U}nveil the {R}oots of {P}lant {C}arnivory}. Palfalvi, G.; Hackl, T.; Terhoeven, N.; Shibata, T. F.; Nishiyama, T.; Ankenbrand, M.; Becker, D.; Förster, F.; Freund, M.; Iosip, A.; Kreuzer, I.; Saul, F.; Kamida, C.; Fukushima, K.; Shigenobu, S.; Tamada, Y.; Adamec, L.; Hoshi, Y.; Ueda, K.; Winkelmann, T.; Fuchs, J.; Schubert, I.; Schwacke, R.; Al-Rasheid, K.; Schultz, J.; Hasebe, M.; Hedrich, R. In Current Biology, 30(12), bll 2312–2320. 2020.
2019[ to top ]
  • Linking pollen foraging of megachilid bees to their nest bacterial microbiota. Voulgari-Kokota, Anna; Ankenbrand, Markus; Grimmer, Gudrun; Steffan-Dewenter, Ingolf; Keller, Alexander. In Ecology and Evolution, 9(18), bll 10788–10800. 2019.
2017[ to top ]
  • AliTV - interactive visualization of whole genome comparisons. Ankenbrand, Markus J.; Hohlfeld, Sonja; Hackl, Thomas; Förster, Frank. In PeerJ Comput. Sci., 3, bl e116. 2017.
  • Maximilian Pfefferle - "Time-resolved image analysis of fluorescent reporters for integrity of bacteria-containing phagosomes" (Master Thesis, Biology), 2024
  • Markus Borel - "Integration der T1-Auswertung klinisch erhobener Messdaten in ein generelles multimodales Framework" (Bachelor Thesis, Informatics), 2023
  • Oliver Kippes - "Machine-based Analysis of Multimodal Cardiovascular Data in the UK-Biobank" (Bachelor Thesis, Biochemistry), 2022
  • Leyla Sırkıntı - Lab practical