Our team


Prof. Dr. Marina Höhne

Head of Department

Department: Dep. Data Science in Bioeconomy

Telefon: +49 (0)331 5699 902
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Program areas

Scientific activities

  • Climate Change and AI in Brandenburg center
  • ELLIS Society, the European Laboratory for Learning and Intelligent Systems
  • Berlin AI Competence Center BIFOLD, the Berlin Institute for the Foundations of Learning and Data



Veröffentlichungen vor ATB-Zugehörigkeit

  • Gautam, S., Höhne, M. M.-C., Hansen, S., Jenssen, R., & Kampffmeyer, M. (2022). Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation. In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) (pp. 1-5). IEEE
  • Hedström, A., Weber, L., Bareeva, D., Motzkus, F., Samek, W., Lapuschkin, S., and Höhne, M. M.-C. (2022). Quantus: an explainable AI toolkit for responsible evaluation of neural network explanations. arXiv preprint arXiv:2202.06861
  • Mieth, B., Rozier, A., Rodriguez, J. A., Höhne, M.M.-C., Görnitz, N., & Müller, K. R. (2021): DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies. NAR Genomics and Bioinformatics, 3(3), lqab065
  • Bykov, K., Deb, M., Grinwald, D., Müller, K.R. and Höhne, M.M.-C., 2022. DORA: Exploring outlier representations in Deep Neural Networks. arXiv preprint arXiv: 2206.04530
  • Bykov, K., Hedström, A., Nakajima, S., and Höhne, M.M.-C. (2021): NoiseGrad: enhancing explanations by introducing stochasticity to model weights. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 6, pp. 6132-6140)
  • Bykov, K., Höhne, M.M.-C., Creosteanu, A., Müller, K.-R., Klauschen, F., Nakajima, S., & Kloft, M. (2021). Explaining bayesian neural networks. arXiv preprint arXiv:2108.10346
  • Mieth, B., Hockley, J. R., Görnitz, N., Vidovic, M.M.-C., Müller, K. R., Gutteridge, A., and Ziemek, D. (2019): Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data. Scientific reports, 9(1), 1-14
  • Vidovic, M.M.-C., Kloft M., Müller K.-R., and Görnitz N., 2017. ML2motif – Reliable extraction of discriminative sequence motifs from learning machines. PloS one 12.3, e0174392
  • Vidovic M.M.-C., Görnitz N., Müller K.-R., and Kloft M., 2016. Feature importance measure for non-linear learning algorithms. arXiv preprint arXiv:1611.07567
  • Vidovic, M.M.-C., Hwang H.J., Amsüss S., Hahne J.M., Farina D., and Müller K.-R. (2016): Improving the robustness of myoelectric pattern recognition for upper limb prostheses by covariate shift adaptation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24.9,  961-970
  • Vidovic, M.M.-C., Görnitz N., Müller K.-R., Rätsch G., and Kloft M. (2015): SVM2Motif Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor. PloS one  10.12, e0144782