Author

Aygul R. Minnegalieva

Oslo University HospitalORCID 0009-0004-0624-0715Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Assay:protein structure· 2 Approach:deep learning· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Puneet RawatiD· 2 shared Victor GreiffiD· 2 shared Emily Engelhart· 1 shared Eugen UrsuiD· 1 shared Eva SmorodinaiD· 1 shared Geir Kjetil SandveiD· 1 shared Helena BritzeiD· 1 shared Joke Gerarda van BemmeliD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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2 shared papersMolecular Biology

Puneet RawatORCID

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2 papers

Recent·Most cited

Effects of protein interface mutations on protein quality and affinity

Jurrian K. de Kanter, Eva Smorodina, Aygul R. Minnegalieva, Marloes Arts, Lasse M. Blaabjerg, Marina Frolenkova, Puneet Rawat, Lina Wolfram, Helena Britze, Yano Wilke, Lucas Matthias Weissenborn, Laurens H. Lindenburg, Emily Engelhart, Kerry L. McGowan, Ryan Emerson, , Victor Greiff

The paper disentangles protein-interaction and protein-quality effects in antibody-antigen binding using deep mutational scanning and evaluates models for their ability to predict these effects.

bioRxiv · Mar 26, 2026 · PreprintMolecular Biology

Training data composition determines machine learning generalization and biological rule discovery

Eugen Ursu, Aygul R. Minnegalieva, Puneet Rawat, Maria Chernigovskaya, Robi Tăcutu, Geir Kjetil Sandve, Philippe Auguste Robert, Victor Greiff

This study shows that the composition of training data, especially negative examples, affects machine learning model performance and biological rule discovery in antibody-antigen binding.

Nature Machine Intelligence · Aug 20, 2025Peer-reviewedBioinformatics