Author

Antonio Lanzavecchia

Institute for Research in BiomedicineORCID 0000-0002-3041-7240Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:CITE-seq· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Research Focus:phylogenetics / evolution· 1 Assay:proteomics· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Mathilde FoglieriniiD· 2 shared Andrea CavalliiD· 1 shared Davide CortiiD· 1 shared Jacopo SgrignaniiD· 1 shared Laurent H. PereziD· 1 shared Leontios PappasiD· 1 shared Luca PiccoliiD· 1 shared Luca VaraniiD· 1 shared

Activity by Year

2020-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersBioinformatics

Mathilde FoglieriniORCID

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2 papers

Recent·Most cited

Machine learning predicts immunoglobulin light chain toxicity through somatic mutations

Maura Garofalo, Luca Piccoli, Margherita Romeo, Maria Monica Barzago, Sara Ravasio, Mathilde Foglierini, Milos Matkovic, Jacopo Sgrignani, Raoul De Gasparo, Marco Prunotto, Luca Varani, Luisa Diomede, Olivier Alain Michielin, Antonio Lanzavecchia, Andrea Cavalli

This paper introduces LICTOR, a machine learning model that predicts immunoglobulin light chain toxicity in AL using somatic mutations, achieving 83% accuracy in predicting clinical phenotypes.

bioRxiv · Dec 15, 2020 · PreprintBioinformatics

AncesTree: an interactive immunoglobulin lineage tree visualizer

Mathilde Foglierini, Leontios Pappas, Antonio Lanzavecchia, Davide Corti, Laurent H. Perez

AncesTree provides an interactive GUI for visualizing immunoglobulin lineage trees and mutations derived from Phylip analysis of antibody repertoires.

PLoS Computational Biology · Jul 10, 2020Peer-reviewedBioinformatics