Author

Max Salm

ORCID 0000-0002-5742-3376Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Organism:mouse· 1 Assay:protein sequence· 1 Approach:statistical model· 1 Research Focus:variant calling· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Andrew Craig· 1 shared Charles SwantoniD· 1 shared Charlotte Boulton· 1 shared Felix O’FarrelliD· 1 shared Hugh O’BrieniD· 1 shared Laura T. MortoniD· 1 shared Laurence King· 1 shared Lee LancashireiD· 1 shared

Activity by Year

2017-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperBioinformatics

Andrew Craig

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintMay 22, 2024

Genesis: A Modular Protein Language Modelling Approach to Immunogenicity Prediction

Hugh O’Brien, Max Salm, Laura T. Morton…·Sergio A. Quezada

Genesis is a modular protein language model that improves immunogenicity prediction for CD8+ epitopes by integrating pMHC and TCR context, outperforming existing models in precision.

DOI 10.1101/2024.05.22.595296

protein sequencedeep learning

𝕏 PostBluesky

GenomicsbioRxivPreprintAug 25, 2017

pdxBlacklist: Identifying artefactual variants in patient-derived xenograft samples

Max Salm, Sven-Eric Schelhorn, Lee Lancashire, Thomas Grombacher

pdxBlacklist identifies false-positive variants in PDX samples by detecting mouse-derived reads mapped to the human genome, improving variant call set quality.

3 citations

mousehuman

𝕏 PostBluesky