Author

Amelie Stalke

Medizinische Hochschule HannoverORCID 0000-0002-8438-8692Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Physiology· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1 Approach:mechanistic / dynamical model· 1 Assay:organoid / primary culture· 1 Assay:protein sequence· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Annika BehrendtiD· 2 shared Carola DrögeiD· 2 shared Holger GohlkeiD· 2 shared Verena KeiteliD· 2 shared Alex Bastianelli· 1 shared Daniel MulnaesiD· 1 shared Eva-Doreen Pfister· 1 shared Filip KoenigiD· 1 shared

Activity by Year

2022-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersPhysiology

Annika BehrendtORCID

Mutual co-authors in cluster

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2 papers

Recent·Most cited

Impaired transitioning of the FXR ligand binding domain to an active state underlies an FXR-associated PFIC phenotype

Annika Behrendt, Alex Bastianelli, Jan Stindt, Eva-Doreen Pfister, Malte Sgodda, Tobias Cantz, Sebastian Hook, Kathrin Grau, Stefanie Brands, Carola Dröge, Amelie Stalke, Michele Bonus, Sabine Franke, Ulrich Baumann, Verena Keitel, Holger Gohlke

This study identifies a missense variant in FXR that impairs its conformational transition to an active state, leading to reduced BSEP expression and a PFIC phenotype.

bioRxiv · Feb 8, 2024 · PreprintPhysiology

Vasor: Accurate prediction of variant effects for amino acid substitutions in MDR3

Annika Behrendt, Pegah Golchin, Filip Koenig, Daniel Mulnaes, Amelie Stalke, Carola Dröge, Verena Keitel, Holger Gohlke

Vasor is a machine learning model that accurately predicts the pathogenicity of MDR3 missense variants, outperforming existing tools and providing a webserver for predictions.

bioRxiv · Feb 20, 2022 · PreprintBioinformatics