Author

Jeffrey Kelvin Mak

University of OxfordORCID 0000-0003-0459-9789Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Assay:CRISPR screen· 1 Assay:proteomics· 1

Frequent Co-Authors

Péter MináryiD· 2 shared Florian StörtziD· 1 shared

Activity by Year

2023-2025 · peak 1/yr

Co-Author Collaboration Network

2 top collaborators · 1 mutual co-author link

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2 shared papersBioinformatics

Péter MináryORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintOct 8, 2025

DeepEmbCas9: Cas9 coevolution and sgRNA structural information for CRISPR-Cas9 cleavage activity prediction

Jeffrey Kelvin Mak, Péter Mináry

This paper introduces DeepEmbCas9 models to predict CRISPR-Cas9 cleavage activity across multiple variants using protein and RNA embeddings, showing improved performance and uncertainty quantification.

1 citation

proteomicsdeep learning

𝕏 PostBluesky

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

𝕏 PostBluesky