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Florian Barkmann on Muck Rack

Florian Barkmann

As seen in: BioRxiv, Genome Research
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Articles

Robust and efficient annotation of cell states through gene signature scoring

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By Laure Ciernik, Agnieszka Kraft, Florian Barkmann
| Genome Research
1,4,6,7 1ETH Zurich, Department of Computer Science, Institute for Machine Learning, 8092 Zurich, Switzerland; 2Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany; 3Hector Fellow Academy, 76131 Karlsruhe, Germany; 4SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; 5University Hospital Zurich, Department of Thoracic Surgery, 8092 Zurich, Switzerland; 6ETH AI Center, ETH Zürich, 8092 Zurich, Switzerland; 7Institut Cochin, Inserm U1016, CNRS UMR 8104,...
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ANS: Adjusted Neighborhood Scoring to improve gene signature-based cell annotation in single-cell RNA-seq data

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By Laure Ciernik, Agnieszka Kraft, Florian Barkmann, Josephine Yates
| BioRxiv
Abstract In the field of single-cell RNA sequencing (scRNA-seq), gene signature scoring is integral for pinpointing and characterizing distinct cell populations. However, challenges arise in ensuring the robustness and comparability of scores across various gene signatures and across different batches and conditions. Here, we evaluated the stability of established methods such as Scanpy, UCell, and JASMINE in the context of scoring cells of different types and states.
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CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer

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By Alexander Theus, Florian Barkmann, David Wissel, Valentina Boeva
| BioRxiv
Abstract We present CancerFoundation, a novel single-cell RNA-seq foundation model (scFM) trained exclusively on malignant cells. Despite being trained on only one million total cells, a fraction of the data used by existing models, CancerFoundation outperforms other scFMs in key tasks such as zero-shot batch integration and drug response prediction.
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