Publications

* Equal contribution.

Selected work

You can find a full list of peer-reviewed papers and preprints below or on my ORCID or Google Scholar profiles, but these five examples give an overview of the main themes of my work so far.

Adding layers of information to scRNA-seq data using pre-trained language models
Krissmer SM, Menger J, Rollin J, Vogel TM, Binder H, and Maren Hackenberg
bioRxiv, 2025. Status: under revision
Preprint on adding language-model-derived information layers to single-cell RNA-seq data.
Preprint · Code
A statistical approach to latent dynamic modeling with differential equations
Maren Hackenberg, Pechmann A, Kreutz C, Kirschner J, and Binder H
The American Statistician, 80(1), 89-99, 2026
Paper describing a latent dynamic modeling framework that combines differential equations with statistical modeling for longitudinal clinical data.
Paper · Code
Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data
Maren Hackenberg*, Canal Guitart L*, Backofen R, and Binder H
Briefings in Bioinformatics, 26(3), 2025
Study evaluating discrepancies between dimensionality reduction approaches for time-series single-cell RNA-seq data.
Paper · Code
Infusing structural assumptions into dimension reduction for single-cell RNA sequencing data to identify small gene sets
Maren Hackenberg*, Brunn N*, Vogel T, and Binder H
Communications Biology, 8, 414, 2025
Paper on encoding structural assumptions into dimension reduction to identify compact gene sets in single-cell RNA sequencing data.
Paper · Code
Using differentiable programming for flexible statistical modeling
Maren Hackenberg, Grodd M, Kreutz C, Fischer M, Esins J, Grabenhenrich L, Karagiannidis C, and Binder H
The American Statistician, 76(3), 270-279, 2022
Paper demonstrating differentiable programming as a flexible implementation strategy for statistical modeling.
Paper · Code

Structured representation learning for single-cell omics

Sparse dimensionality reduction for analyzing single-cell-resolved interactions
Brunn N, Maren Hackenberg, Camila L Fullio, Vogel T, and Binder H
Bioinformatics Advances, 6(1), vbag047, 2026
Paper · Code
Mapping spatial cell-cell communication programs by tailoring chains of cells for transformer neural networks
Brunn N, Guitart LC, Farhadyar K, Fullio CL, Kailer J, Vogel T, Maren Hackenberg, and Binder H
bioRxiv, 2026. Status: under revision
Preprint · Code
Embedding interpretable l1-regression into neural networks for uncovering temporal structure in cell imaging
Kabus F, Maren Hackenberg, Hindel J, Cholvin T, Kilias A, Brox T, Valada A, Bartos M, and Binder H
arXiv, 2026. Status: under review
Preprint
mmContext: an open framework for multimodal contrastive learning of omics and text data
Jonatan Menger, Sonia Maria Krissmer, Clemens Kreutz, Harald Binder, and Maren Hackenberg
Bioinformatics, 42(6), btag338, 2026
Paper · Code
Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data
Maren Hackenberg*, Canal Guitart L*, Backofen R, and Binder H
Briefings in Bioinformatics, 26(3), 2025
Paper · Code
Infusing structural assumptions into dimension reduction for single-cell RNA sequencing data to identify small gene sets
Maren Hackenberg*, Brunn N*, Vogel T, and Binder H
Communications Biology, 8, 414, 2025
Paper · Code
Adding layers of information to scRNA-seq data using pre-trained language models
Krissmer SM, Menger J, Rollin J, Vogel TM, Binder H, and Maren Hackenberg
bioRxiv, 2025. Status: under revision
Preprint · Code
scSpecies: enhancement of network architecture alignment in comparative single-cell studies
Schächter C, Maren Hackenberg, Treppner M, Raum H, Bödecker J, and Binder H
Genome Biology, 26, 397, 2025
Paper · Code
The performance of deep generative models for learning joint embeddings of single-cell multi-omics data
Brombacher E*, Maren Hackenberg*, Kreutz C, Binder H, and Treppner M
Frontiers in Molecular Biosciences, 9, 2022
Paper · Code
Incorporating structural knowledge into unsupervised deep learning for two-photon imaging data
Eichin F*, Maren Hackenberg*, Broichhagen C, Kilias A, Schmoranzer J, Bartos M, and Binder H
bioRxiv, 2021
Preprint · Code
Exploring generative deep learning for omics data using log-linear models
Hess M, Maren Hackenberg, and Binder H
Bioinformatics, 36(20), 5045-5053, 2020
Paper · Code

Methods for dynamic modelling of disease trajectories in clinical cohorts

A statistical perspective on transformers for small longitudinal cohort data
Farhadyar K, Maren Hackenberg, Ahrens K, Schenk C, Kollmann B, Tüscher O, Lieb K, Plichta MM, Reif A, Kalisch R, Wolkewitz M, Hess M, and Binder H
arXiv, 2026. Status: revision submitted
Preprint · Code
A statistical approach to latent dynamic modeling with differential equations
Maren Hackenberg, Pechmann A, Kreutz C, Kirschner J, and Binder H
The American Statistician, 80(1), 89-99, 2026
Paper · Code
Using latent representations to link disjoint longitudinal data for mixed-effects regression
Schächter C, Maren Hackenberg, Pfaffenlehner M, Tambe-Ndonfack FB, Schmidt T, Pechmann A, Kirschner J, Hasenauer J, and Binder H
Statistics in Medicine, 45(18-19), e70701, 2026
Paper · Preprint
Investigating a domain adaptation approach for integrating different measurement instruments in a longitudinal clinical registry
Maren Hackenberg, Pfaffenlehner M, Behrens M, Pechmann A, Kirschner J, and Binder H
Biometrical Journal, 2024
Paper · Code
Deep dynamic modeling with just two time points: Can we still allow for individual trajectories?
Maren Hackenberg, Harms P, Pfaffenlehner M, Pechmann A, Kirschner JB, Schmidt T, and Binder H
Biometrical Journal, 64(8), 1426-1445, 2022
Paper · Code

Statistical computing and scientific software

Using differentiable programming for flexible statistical modeling
Maren Hackenberg, Grodd M, Kreutz C, Fischer M, Esins J, Grabenhenrich L, Karagiannidis C, and Binder H
The American Statistician, 76(3), 270-279, 2022
Paper · Code
The JuliaConnectoR: a functionally-oriented interface for integrating Julia in R
Lenz S, Maren Hackenberg, and Binder H
Journal of Statistical Software, 101(6), 1-24, 2022
Paper · Code

Translational and clinical collaborations

Diagnostic clues and pitfalls in pontocerebellar hypoplasia type 2A
Herrmann A, Kuhn A, Maren Hackenberg, Matilainen J, Mayer S, Groeschel S, Uhl M, Krägeloh-Mann I, and Janzarik WG
Pediatric Neurology, 178, 186-194, 2026
Paper
Systematic benchmarking of CRISPR-Cas9 off-target prediction tools reveals limitations and implications for preclinical assessment
Kaufmann MM, Maren Hackenberg, Pargeter WJ, Backofen R, Binder H, and Cathomen T
Human Gene Therapy, 2026. Status: published online ahead of issue
Paper
Constructed growth charts and nutrition for pontocerebellar hypoplasia type 2A
Kuhn A, Maren Hackenberg, Klauser AL, Herrmann A, Matilainen J, Mayer S, Frölich S, Krägeloh-Mann I, Groeschel S, and Janzarik WG
Developmental Medicine & Child Neurology, 68, 82-90, 2026
Paper
Machine learning-based prediction of one-year mortality after alloHCT identifies the impact of pre-transplant immunity and inflammation
Meyer T, Meyer R, Maren Hackenberg, Oelke D, Gengenbach L, Rummelt C, Wilcken H, Maas-Bauer K, Wäsch R, Duyster J, Bertz H, Duque-Afonso J, Finke J, Zeiser R, and Wehr C
Frontiers in Immunology, 16, 2026
Paper
Brain morphometry and psychomotor development in children with PCH2A
Pretzel P, Herrmann A, Kuhn A, Klauser AL, Matilainen J, Elias Kellner, Maren Hackenberg, Mayer S, Laugwitz L, Uhl M, Groeschel S, and Janzarik W
European Journal of Paediatric Neurology, 56, 58-66, 2025
Paper
Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent subgroups
Farhadyar K, Bonofiglio F, Maren Hackenberg, Zöller D, and Binder H
BMC Medical Research Methodology, 24(1), 198, 2024
Paper
Prognosemodelle zur Steuerung von intensivmedizinischen COVID-19-Kapazitäten in Deutschland
Grodd M, Refisch L, Lorenz F, Fischer M, Lottes M, Maren Hackenberg, Kreutz C, Grabenhenrich L, Binder H, and Wolkewitz M
Medizinische Klinik - Intensivmedizin und Notfallmedizin, 2022
Paper

Conceptual and policy-oriented outputs

Small Data Explainer - The impact of small data methods in everyday life
Maren Hackenberg, Connor SG, Kabus F, Brawner J, Markham E, Hardalupas M, Chowdhury A, Backofen R, Köttgen A, Rohde A, Binder N, Binder H, and the Collaborative Research Center 1597 Small Data
arXiv, 2025. Status: revision submitted; in collaboration with the Royal Society; contributing to the Disability Technology report
Preprint · Report