Anne Hartebrodt

Dr. Anne Hartebrodt

Postdoctoral Researcher

Department Artifical Intelligence in Biomedical Engineering (AIBE)
Biomedical Network Science Lab

Werner-von-Siemens-Str. 61
91052 Erlangen

Academic CV

  • Since March 2023 : Postdoctoral resesearcher at the Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany. Studying Network based embeddings and carbon-aware computing for bioinformatics
  • March 2022 – February 2023: Research assistant at University of Southern Denmark and Odense University Hospital (OUH)
  • February 2019 – February 2022: PhD in Bioinformatics at University of Southern Denmark (SDU), Odense, Denmark. Thesis entitled Federated Unsupervised Machine Learning.
  • October 2016 – December 2018 : Master in Bioinformatics at Technical University Munich (TUM) and Ludwig Maximilian University (LMU) Munich
  • October 2012 – October 2016 : Bachelor in Bioinformatics at Technical University Munich (TUM) and Ludwig Maximilian University (LMU) Munich







  • Dimensionality reduction for molecular data based on explanatory power of differential regulatory networks

    (Third Party Funds Group – Overall project)

    Term: 01/03/2023 - 28/02/2026
    Funding source: Bundesministerium für Bildung und Forschung (BMBF)

    Rapid advances in single-cell RNA sequencing (scRNA-seq) technology are leading to ever-increasing dimensions of the generated molecular data, which complicates data analyses. In NetMap, new scalable and robust dimensionality reduction approaches for scRNA-seq data will be developed. To this end, dimensionality reduction will be integrated into a central task of the systems medicine analysis of scRNA-seq data: inference of gene regulatory networks (GRNs) and driver transcription factors based on cell expression profiles. Each resulting dimension will correspond to a driver GRN, and the coordinate of a cell in this low-dimensional representation will quantify the extent to which the particular driver GRN explains the cell's gene expression profile. These new methods will be implemented as a user-friendly software platform for exploratory expert-in-the-loop analysis and in silico prediction of drug repurposing candidates.

    As a case study, we will investigate CD4 helper T cell exhaustion, a potential limiting factor in immunotherapy. NetMap's strategy consists of (1) analyzing phenotypic heterogeneity of depleted CD4 T cells, (2) identifying transcriptional mechanisms that control this heterogeneity, (3) amplifying/eliminating specific subsets and testing their functional impact. This will allow the development of an atlas of the gene regulatory landscape of depleted CD4 T cells, while the in vivo testing of key regulatory transcription factors will help demonstrate the power of the developed methods and allow evaluation and improvement of predictions.