Author

Pegah Golchin

Technische Universität DarmstadtORCID 0000-0002-8686-113XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Organism:human· 1 Approach:machine learning (classical)· 1 Assay:protein sequence· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Amelie StalkeiD· 1 shared Annika BehrendtiD· 1 shared Carola DrögeiD· 1 shared Daniel MulnaesiD· 1 shared Filip KoenigiD· 1 shared Holger GohlkeiD· 1 shared Verena KeiteliD· 1 shared

Activity by Year

2022-2022 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Amelie StalkeORCID

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1 paper

Recent·Most cited

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