Data Science / Representation Learning and Data Visualization
We develop machine learning tools for visualizing high-dimensional data such as single cell transcriptomics, clinical images, or scientific texts.
01
González-Márquez, R., Schmidt, L., Schmidt, B. M., Berens, P., & Kobak, D.
The landscape of biomedical research
02
Nazari, P., Damrich, S., Hamprecht, F.A.
Geometric Autoencoders – What You See is What You Decode
03
Böhm, J. N., Berens, P., & Kobak, D.
Unsupervised visualization of image datasets using contrastive learning
04
Damrich, S., Böhm, J. N., Hamprecht, F. A., & Kobak, D.
From t-SNE to UMAP with contrastive learning
05
Bachmann, F., Hennig, P., Kobak, D.
Wasserstein tSNE
06
Böhm, J. N., Berens, P., & Kobak, D.
Attraction-repulsion spectrum in neighbor embeddings
07
Scala, F., Kobak, D., Bernabucci, M., Bernaerts, Y., Cadwell, C. R., Castro, J. R., [...], Berens, P. & Tolias, A. S.
Phenotypic variation of transcriptomic cell types in mouse motor cortex
08
Ilanchezian, I., Kobak, D., Faber, H., Ziemssen, F., Berens, P., & Ayhan, M. S
Interpretable gender classification from retinal fundus images using BagNets.
09
Lause, J., Berens, P., & Kobak, D.
Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data.
10
Kobak, D., Bernaerts, Y., Weis, M. A., Scala, F., Tolias, A. S., & Berens, P.
Sparse reduced-rank regression for exploratory visualisation of paired multivariate data
11
Karlinsky, A., & Kobak, D.
Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset.
12
Kobak, D., & Linderman, G. C.
Initialization is critical for preserving global data structure in both t-SNE and UMAP
13
Kobak, D., & Berens, P.
The art of using t-SNE for single-cell transcriptomics.
14
Kobak, D., Linderman, G., Steinerberger, S., Kluger, Y., & Berens, P.