Author

Flavia Corsi

Centre National de la Recherche ScientifiqueORCID 0000-0002-8461-2869Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Assay:protein structure· 2 Approach:statistical model· 2 Research Focus:database / resource· 1 Approach:experimental assay· 1 Assay:Hi-C· 1 Organism:human· 1 Research Focus:protein structure prediction· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Alessandra CarboneiD· 2 shared Élodie LaineiD· 2 shared Anton A. GoloborodkoiD· 1 shared Daniel W. GerlichiD· 1 shared Michael MitteriD· 1 shared Paul BattyiD· 1 shared Paul SaighiiD· 1 shared Richard LaveryiD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersBioinformatics

Alessandra CarboneORCID

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3 papers

Recent·Most cited

Conformational asymmetry of replicated human chromosomes

Flavia Corsi, Sofia Kolesnikova, Thomas L. Steinacker, Zsuzsanna Takács, Paul Batty, Michael Mitter, Daniel W. Gerlich, Anton A. Goloborodko

The paper reveals a genome-wide asymmetry in sister chromatid alignment due to replication fork asymmetry, maintained by cohesive cohesins.

Science · Sep 3, 2026Peer-reviewedMolecular Biology

LEVELNET to visualise, explore and compare protein-protein interaction networks

Yasser Mohseni Behbahani, Paul Saighi, Flavia Corsi, Élodie Laine, Alessandra Carbone

LEVELNET is a tool for visualizing, exploring, and comparing protein-protein interaction networks using multilayered graphs and structural data from the Protein Data Bank.

bioRxiv · Jul 31, 2021 · PreprintBioinformatics

Multiple protein-DNA interfaces unravelled by evolutionary information, physico-chemical and geometrical properties

Flavia Corsi, Richard Lavery, Élodie Laine, Alessandra Carbone

The paper introduces a method to predict protein-DNA interfaces using evolutionary, physico-chemical, and geometric properties, achieving higher sensitivity than existing methods.

PLoS Computational Biology · Feb 3, 2020Peer-reviewedBioinformatics