Author

Judit Szarvas

Technical University of DenmarkORCID 0000-0002-0720-3017Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 2 Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Approach:machine learning (classical)· 1 Research Focus:phylogenetics / evolution· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Frank M. AarestrupiD· 3 shared Ole LundiD· 2 shared Ahmad ZyoudiD· 1 shared Alexey SokoloviD· 1 shared Bas B. Oude MunninkiD· 1 shared Björn Andreas GrüningiD· 1 shared Carla A. CumminsiD· 1 shared Clara AmidiD· 1 shared

Activity by Year

2020-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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3 shared papersBioinformatics

Frank M. AarestrupORCID

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3 papers

Recent·Most cited

Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses

Nadim Rahman, Colman O’Cathail, Ahmad Zyoud, Alexey Sokolov, Bas B. Oude Munnink, Björn Andreas Grüning, Carla A. Cummins, Clara Amid, David F. Nieuwenhuijse, Dávid Visontai, David Yu Yuan, Dipayan Gupta, Divyae K. Prasad, Gábor Máté Gulyás, Gabriele Rinck, , Guy Cochrane

The paper describes open data sharing, analysis tools, and lessons learned from the European COVID-19 Data Platform's SARS-CoV-2 data collection and analysis efforts.

Microbial Genomics · Feb 15, 2024Peer-reviewedBioinformatics

PlasmidHostFinder: Prediction of plasmid hosts using random forest

Derya Aytan-Aktug, Philip T. L. C. Clausen, Judit Szarvas, Patrick Munk, Saria Otani, Marcus Nguyen, James J. Davis, Ole Lund, Frank M. Aarestrup

This paper develops a random forest model to predict plasmid hosts, achieving 0.662 and 0.867 Matthews correlation coefficients at species and order levels, respectively.

mSystems · Apr 6, 2022Peer-reviewedBioinformatics

Large scale automated phylogenomic analysis of bacterial whole-genome isolates and the Evergreen platform

Judit Szarvas, Johanne Ahrenfeldt, Jose Luis Bellod Cisneros, Martin Christen Frølund Thomsen, Frank M. Aarestrup, Ole Lund

The paper introduces a bioinformatics pipeline for rapid phylogenomic analysis of bacterial whole-genome isolates, enabling large-scale surveillance and real-time outbreak detection.

Communications Biology · Mar 20, 2020Peer-reviewedBioinformatics