Keywords:

Machine Learning, Deep Learning, Integration of Molecular and Clinical Data

10 selected publications:

• The performance of deep generative models for learning joint embeddings of single-cell multi-omics data.
  Brombacher E, Hackenberg M, Kreutz C, Binder H, Treppner M. (2022). ,
  Frontiers in Molecular Biosciences 962644.
• Using Differentiable Programming for Flexible Statistical Modeling.
  Hackenberg M, Grodd M, Kreutz C, Fischer M, Esins J, Grabenhenrich L, Karagiannidis C, Binder H. (2022).
  The American Statistician 76(3):270–9.
• Stratified neural networks in a time-to-event setting.
  Kuruc F, Binder H, Hess M. (2022)
  Brief Bioinform; 23(1):1–11.
• Interpretable generative deep learning: an illustration with single cell gene expression data.
  Treppner M, Binder H, Hess M. (2022)
  Human Genetics; 141:1481–98.
• Synthetic observations from deep generative models and binary omics data with limited sample size.
  Nußberger J, Boesel F, Lenz S, Binder H, Hess M. (2021)
  Brief Bioinform;22(4):1–12.
• Synthetic single cell RNA sequencing data from small pilot studies using deep generative models.
  Treppner M, Salas-Bastos A, Hess M, Lenz S, Vogel T, and Binder H. (2021)
  Scientific Reports; 11:9403.
• Exploring generative deep learning for omics data by using log-linear models.
  Hess M, Hackenberg M, and Binder H. (2020)
  Bioinformatics; 36:5045–53.
• Bioconductor package for interactive differential expression analysis.
  Marini F, Linke J, and Binder H. (2020)
  BMC Bioinformatics; 21:565.
• Partitioned learning of deep Boltzmann machines for SNP data.
  Hess M, Lenz S, Blätte TJ, Bullinger L, and Binder H. (2017)
  Bioinformatics; 33:3173–80.
• Feasibility of sample size calculation for RNA-seq studies.
  Poplawski A and Binder H. (2017)
  Briefings in Bioinformatics; 19:713–20.