Author

Francesca Minnai

Institute of Biomedical TechnologiesORCID 0000-0003-3646-3800Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetic & Genomic Medicine· 2 Genetics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 2 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1

Frequent Co-Authors

Axel SchmidtiD· 2 shared Francesca ColomboiD· 2 shared Abdou MousasiD· 1 shared Abigail Miller· 1 shared Agnese Verzuri· 1 shared Agostino OgnibeneiD· 1 shared Agostino RivaiD· 1 shared Alberto VannelliiD· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Axel SchmidtORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Genetic & Genomic MedicineNature GeneticsPeer-reviewedMay 21, 2025

Genome-wide Association Study of Long COVID

Vilma Lammi, Tomoko Nakanishi, Samuel E. Jones…·Sergi Papiol

This study performs a genome-wide association study for Long COVID, identifying a significant association with the FOXP4 locus and highlighting the role of pulmonary dysfunction and COVID-19 severity.

86 citationstop 1%

humanGWAS summary statistics

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GeneticsScientific ReportsPeer-reviewedJul 6, 2022

Gut microbiota composition in colorectal cancer patients is genetically regulated

Francesca Colombo, Oscar Illescas, Sara Noci…·Manuela Gariboldi

This study identifies germline variants associated with gut microbiota composition in CRC patients and shows they influence both microbiota and inflammatory gene expression, potentially affecting CRC risk.

24 citations

humanmetagenomics

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Genetic & Genomic MedicineHuman GeneticsPeer-reviewedDec 10, 2021

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

Chiara Fallerini, Nicola Picchiotti, Margherita Baldassarri…·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.

37 citationstop 5%

humanwhole-exome sequencing

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