Author

Anja Gumpinger

SIB Swiss Institute of BioinformaticsORCID 0000-0003-0194-1809Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Synthetic Biology· 1

Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 1 Approach:deep learning· 1 Assay:DNA methylation· 1 Research Focus:sequence-to-function modeling· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Christian BeiseliD· 1 shared Karsten Michael BorgwardtiD· 1 shared Katrin FischeriD· 1 shared Laetitia Meng-PapaxanthosiD· 1 shared Markus JeschekiD· 1 shared Simon HöllereriD· 1 shared Yaakov BenensoniD· 1 shared

Activity by Year

2020-2020 · peak 1/yr

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7 top collaborators · 21 mutual co-author links

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1 shared paperSynthetic Biology

Christian BeiselORCID

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1 paper

Recent·Most cited

Synthetic BiologyNature CommunicationsPeer-reviewedJul 15, 2020

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping

Simon Höllerer, Laetitia Meng-Papaxanthos, Anja Gumpinger…·Markus Jeschek

The paper introduces DNA-based phenotypic recording to generate large-scale sequence-function datasets and uses deep learning to predict gene regulatory element function with high accuracy.

68 citations

bacteria (other)DNA methylation

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