Author

Julia Gimbernat-Mayol

Imperial College London

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1 Genetic & Genomic Medicine· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Research Focus:clustering· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Organism:multiple species· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Alexander G. IoannidisiD· 2 shared Daniel Mas MontserratiD· 2 shared Aigerim RymbekovaiD· 1 shared Albert Dominguez MantesiD· 1 shared Alice B. PopejoyiD· 1 shared Carlos D. BustamanteiD· 1 shared Cayetana López· 1 shared Emily R. BartusiakiD· 1 shared

Activity by Year

2021-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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2 shared papersBioinformatics

Alexander G. IoannidisORCID

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Author profile →

3 papers

Recent·Most cited

Archetypal Analysis for Population Genetics

Julia Gimbernat-Mayol, Albert Dominguez Mantes, Carlos D. Bustamante, Daniel Mas Montserrat, Alexander G. Ioannidis

This paper introduces Archetypal Analysis for efficient genetic clustering, showing it outperforms ADMIXTURE in speed and interpretability across human and canid datasets.

PLoS Computational Biology · Aug 25, 2022Peer-reviewedBioinformatics

Predicting Dog Phenotypes from Genotypes

Emily R. Bartusiak, Míriam Barrabés, Aigerim Rymbekova, Julia Gimbernat-Mayol, Cayetana López, Lorenzo Barberis, Daniel Mas Montserrat, Xavier Giró-i-Nieto, Alexander G. Ioannidis

This paper predicts dog phenotypes (breed, height, weight) from genotypes using linear and non-linear methods, showing that linear methods outperform non-linear for breed classification but not for height and weight regression.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · Jul 11, 2022Peer-reviewedGenomics

Clinical genetics laboratories use divergent demographic frameworks across countries: comparing data structures for race, ethnicity, and ancestry on test requisition forms

Alice B. Popejoy, Julia Gimbernat-Mayol, Katherine C. Anderson, Gillian W. Hooker

This study compares how clinical genetics labs in different countries represent race, ethnicity, and ancestry on test requisition forms, finding significant differences in data collection practices.

medRxiv · Dec 17, 2021 · PreprintGenetic & Genomic Medicine