Author

Roberto Cahuantzi

University of ManchesterORCID 0000-0002-0212-6825Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 1 Research Focus:variant calling· 1 Organism:virus· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Ian HalliD· 1 shared Katrina LythgoeiD· 1 shared Lorenzo PellisiD· 1 shared Thomas HouseiD· 1 shared

Activity by Year

2024-2024 · peak 1/yr

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4 top collaborators · 6 mutual co-author links

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1 shared paperBioinformatics

Ian HallORCID

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1 paper

Recent·Most cited

BioinformaticsProceedings of the National Academy of SciencesPeer-reviewedMar 13, 2024

Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods

Roberto Cahuantzi, Katrina Lythgoe, Ian Hall…·Thomas House

This paper develops scalable machine learning methods to identify significant SARS-CoV-2 lineages from genomic data, offering a complementary approach to traditional phylogenetic analysis.

4 citations

viruswhole-genome sequencing

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