Author

Florian Störtz

University of OxfordORCID 0000-0002-4095-3450Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Assay:CRISPR screen· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1

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Jeffrey Kelvin MakiD· 1 shared Péter MináryiD· 1 shared

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2023-2023 · peak 1/yr

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2 top collaborators · 1 mutual co-author link

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1 shared paperBioinformatics

Jeffrey Kelvin MakORCID

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1 paper

Recent·Most cited

BioinformaticsArtificial Intelligence in the Life SciencesPeer-reviewedMay 15, 2023

piCRISPR: Physically Informed Deep Learning Models for CRISPR/Cas9 Off-Target Cleavage Prediction

Florian Störtz, Jeffrey Kelvin Mak, Péter Mináry

piCRISPR uses physically informed deep learning to predict CRISPR/Cas9 off-target cleavage, showing improved performance over existing methods by incorporating sequence context and chromatin accessibility.

21 citations

CRISPR screendeep learning

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