Associate Investigators

Prof. Dr. Harald Binder

Prof. Dr. Harald Binder

Kontakt

Prof. Dr. Harald Binder
(he/him)

Institute of Medical Biometry and Statistics (IMBI),
University Medical Center — Faculty of Medicine

T +49 (0)761 270 83744
harald.binder(at)uniklinik-freiburg.de

Weitere Informationen

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.