Author

Christof Angermueller

European Bioinformatics InstituteORCID 0000-0003-0507-1839Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Assay:DNA methylation· 2 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1

Frequent Co-Authors

Heather J LeeiD· 2 shared Oliver StegleiD· 2 shared Wolf ReikiD· 2 shared Anissa Martinez· 1 shared Babak AlipanahiiD· 1 shared Benjamin D. SimonsiD· 1 shared Benjamin W. JesteriD· 1 shared Cameron Cordray· 1 shared

Activity by Year

2017-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersGenomics

Heather J LeeORCID

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintDec 1, 2023

High-throughput ML-guided design of diverse single-domain antibodies against SARS-CoV-2

Christof Angermueller, Zelda Mariet, Benjamin W. Jester…·Lucy J. Colwell

This study uses ML to design diverse VHH antibodies that neutralize SARS-CoV-1 and SARS-CoV-2 variants, including Delta and Omicron BA.1.

6 citations

machine learning (classical)drug response / drug discovery

𝕏 PostBluesky

GenomicsCell SystemsPeer-reviewedJul 1, 2018

Genome-scale oscillations in DNA methylation during exit from pluripotency

Steffen Rulands, Heather J Lee, Stephen J. Clark…·Wolf Reik

The paper shows that during exit from pluripotency, coexpression of DNMT3s and TETs drives genome-scale DNA methylation oscillations, with amplitude dependent on CpG density, observed both in vitro and in vivo.

83 citationstop 5%

mouseDNA methylation

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BioinformaticsGenome biologyPeer-reviewedApr 10, 2017

Accurate prediction of single-cell DNA methylation states using deep learning

Christof Angermueller, Heather J Lee, Wolf Reik, Oliver Stegle

This paper introduces DeepCpG, a deep learning method to predict single-cell DNA methylation states from sequence and incomplete profiles, showing improved accuracy over existing methods.

533 citationstop 1%

humanDNA methylation

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