Author

Marco Tanfoni

University of SienaORCID 0009-0002-2007-2602Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:differential expression· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Agnese Verzuri· 1 shared Agostino OgnibeneiD· 1 shared Agostino RivaiD· 1 shared Alessandra VergoriiD· 1 shared Alessandro PancrazziiD· 1 shared Alice Donati· 1 shared Andrea AntinoriiD· 1 shared Andrea CossarizzaiD· 1 shared

Activity by Year

2021-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperGenetic & Genomic Medicine

Agnese Verzuri

Mutual co-authors in cluster

Author profile →

1 paper

Recent·Most cited

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.

Chiara Fallerini, Nicola Picchiotti, Margherita Baldassarri, Kristina Zguro, Sergio Daga, Francesca Fava, Elisa Benetti, Sara Amitrano, Mirella Bruttini, Maria Palmieri, Susanna Croci, Mirjam Lista, Giada Beligni, Floriana Valentino, Ilaria Meloni, , Pier Giorgio Scotton

The paper develops a machine learning model to predict COVID-19 severity using exonic variants from whole exome sequencing data, finding that variants across all frequency categories contribute to severity.

Human Genetics · Dec 10, 2021Peer-reviewedGenetic & Genomic Medicine