Author

Sebastian Proft

Humboldt-Universität zu BerlinORCID 0000-0002-1707-0625Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Assay:ChIP-seq· 2 Organism:human· 2 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Dominik SeelowiD· 2 shared Udo HeinemanniD· 2 shared Freyda Lenihan-GeelsiD· 1 shared Heiko KrudeiD· 1 shared Janna LeiziD· 1 shared Kai Martin Schmidt-OttiD· 1 shared M.Z. Malecka· 1 shared Maria RutkiewicziD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersNeuroscience

Dominik SeelowORCID

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2 papers

Recent·Most cited

Quantification of the effects of single nucleotide variants in NKX2.1 transcription factor binding sites

Freyda Lenihan-Geels, Sebastian Proft, Martin Bommer, Udo Heinemann, Dominik Seelow, Robert Opitz, Heiko Krude, Markus Schuelke, M.Z. Malecka

The paper quantifies how single nucleotide variants affect NKX2.1 transcription factor binding and uses neural networks to predict disease-causing variants in regulatory regions.

bioRxiv · Mar 2, 2026 · PreprintNeuroscience

Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networks

Sebastian Proft, Janna Leiz, Udo Heinemann, Dominik Seelow, Kai Martin Schmidt-Ott, Maria Rutkiewicz

The paper uses deep learning to identify non-canonical GRHL1 binding sites not detected by traditional PWM methods.

BMC Genomics · Dec 4, 2023Peer-reviewedBioinformatics