Author

Michelle L. Gill

Nvidia (United Kingdom)ORCID 0000-0002-2861-355XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 1 Assay:protein sequence· 1 Assay:proteomics· 1 Research Focus:sequence-to-function modeling· 1 Research Focus:software / tool· 1 Approach:statistical model· 1 Approach:transformer / foundation model· 1 Approach:variational autoencoder· 1

Frequent Co-Authors

Adrian Lange· 1 shared Andrew L. FergusoniD· 1 shared Anthony Beardsworth CostaiD· 1 shared Claire M. BuchananiD· 1 shared Clayton M. DilksiD· 1 shared Constantin AmmariD· 1 shared David J. Hosfield· 1 shared Emre Sevgen· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperSynthetic Biology

Adrian Lange

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2 papers

Recent·Most cited

ProT-VAE: Protein Transformer Variational AutoEncoder for Functional Protein Design

Emre Sevgen, Joshua Moller, Adrian Lange, John Parker, Sean D. Quigley, Jeff Mayer, Poonam Srivastava, Sitaram Gayatri, David J. Hosfield, Clayton M. Dilks, Claire M. Buchanan, Thomas Speltz, Maria V. Korshunova, Micha Livne, Michelle L. Gill, , Andrew L. Ferguson

ProT-VAE designs functional proteins by learning sequence-function relationships using a transformer-based variational autoencoder, enabling conditional generation of novel sequences with high functionality.

Proceedings of the National Academy of Sciences · Oct 6, 2025Peer-reviewedSynthetic Biology

AlphaPept, a modern and open framework for MS-based proteomics

Maximilian T. Strauss, Isabell Bludau, Wen‐Feng Zeng, Eugenia Voytik, Constantin Ammar, Julia Patricia Schessner, Rajesh Ilango, Michelle L. Gill, Florian Meier, Sander Willems, Matthias Mann

AlphaPept is an open-source framework for efficient processing of large MS-based proteomics datasets, achieving significant speed improvements using Python and machine learning.

Nature Communications · Mar 9, 2024Peer-reviewedBioinformatics