Barsch M, Salié H, Schlaak AE, Zhang Z, Hess M, Mayer LS, Tauber C, Otto-Mora P, Ohtani T, Nilsson T, et al. T-cell exhaustion and residency dynamics inform clinical outcomes in hepatocellular carcinoma. Journal of Hepatology 2022;77:397–409.
Kuruc F, Binder H, and Hess M. Stratified neural networks in a time-to-event setting. Briefings in Bioinformatics 2022;23:bbab392.
Treppner M, Binder H, and Hess M. Interpretable generative deep learning: An illustration with single cell gene expression data. Human Genetics 2022;141:1481–1498.
Lenz S, Hess M, and Binder H. Deep generative models in DataSHIELD. BMC Medical Research Methodology 2021;21:64.
Hess M, Hackenberg M, and Binder H. Exploring generative deep learning for omics data by using log-linear models. Bioinformatics 2020;36:5045–5053.
Nußberger J, Boesel F, Lenz S, Binder H, and Hess M. Synthetic observations from deep generative models and binary omics data with limited sample size. Briefings in Bioinformatics 2020;22:bbaa226.
Reimer A, Hess M, Schwieger-Briel A, Kiritsi D, Schauer F, Schumann H, Bruckner-Tuderman L, and Has C. Natural history of growth and anaemia in children with epidermolysis bullosa: a retrospective cohort study. British Journal of Dermatology 2020;182:1437–1448.
Hess M, Lenz S, Blätte TJ, Bullinger L, and Binder H. Partitioned learning of deep Boltzmann machines for SNP data. Bioinformatics 2017;33:3173–3180.
Hess M, Wildhagen H, Junker LV, and Ensminger I. Transcriptome responses to temperature, water availability and photoperiod are conserved among mature trees of two divergent Douglas-fir provenances from a coastal and an interior habitat. BMC Genomics 2016;17:1–18.
Poplawski A, Marini F, Hess M, Zeller T, Mazur J, and Binder H. Systematically evaluating interfaces for RNA-seq analysis from a life scientist perspective. Briefings in Bioinformatics 2016;17:213–223.
Institute of Medical Biometry and Statistics,
Faculty of Medicine and Medical Center –
University of Freiburg
