Author

Kimberly J. Gilbert

ETH ZurichORCID 0000-0002-8507-2294Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:whole-genome sequencing· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Organism:multiple species· 1 Research Focus:phylogenetics / evolution· 1

Frequent Co-Authors

Adrian AltenhoffiD· 1 shared Alex Warwick VesztrocyiD· 1 shared Charles BernardiD· 1 shared Christophe DessimoziD· 1 shared Clément Marie Train· 1 shared Leo ZeitleriD· 1 shared Naga Bhushana Rao KarampudiiD· 1 shared Natasha GloveriD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 28 mutual co-author links

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1 shared paperEvolutionary Biology

Adrian AltenhoffORCID

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2 papers

Recent·Most cited

Evolutionary BiologyNature Ecology & EvolutionPeer-reviewedAug 19, 2025

EdgeHOG: fine-grained ancestral gene order inference at tree-of-life scale

Charles Bernard, Yannis Nevers, Naga Bhushana Rao Karampudi…·Christophe Dessimoz

EdgeHOG infers ancestral gene order across the tree of life, producing 1,133 ancestral genomes and revealing conserved and rapidly evolving gene adjacencies.

4 citations

multiple speciesDNA methylation

𝕏 PostBluesky

GeneticsGenome Biology and EvolutionPeer-reviewedJun 27, 2024

Using runs of homozygosity and machine learning to disentangle sources of inbreeding and infer self-fertilization rates

Leo Zeitler, Kimberly J. Gilbert

The paper uses ROHs and machine learning to distinguish inbreeding sources and estimate self-fertilization rates, showing success even with complex demography.

14 citations

whole-genome sequencingmachine learning (classical)

𝕏 PostBluesky