Author

Giorgia Gandolfi

IRCCS Ospedale San RaffaeleORCID 0009-0007-0154-0884Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:whole-genome sequencing· 2 Assay:ATAC-seq· 1 Research Focus:batch correction / integration· 1 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Giovanni TononiD· 3 shared Aedín C. CulhaneiD· 1 shared Alberto CasagrandeiD· 1 shared Alberto CazzanigaiD· 1 shared Alessio AnsuiniiD· 1 shared Alfonso ValenciaiD· 1 shared Alice Antonello· 1 shared Anders EdsjöiD· 1 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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3 shared papersBioinformatics

Giovanni TononORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJun 11, 2026

Tumour evolution as ground truth for cancer whole-genome sequencing

Valeriani Lucrezia, Giorgia Gandolfi, Elena Buscaroli…·Giulio Caravagna

This paper introduces SCOUT, a synthetic whole-genome sequencing resource for benchmarking cancer genomic analysis, revealing performance issues in complex tumours and highlighting the importance of evolutionary ground truth.

DOI 10.64898/2026.06.09.731105

humanwhole-genome sequencing

𝕏 PostBluesky

Cancer BiologyNature GeneticsPeer-reviewedMay 1, 2024

The Minimal Dataset for Cancer of the 1+Million Genomes Initiative

Michela Riba, Cinzia Felicita Sala, Aedín C. Culhane…·Giovanni Tonon

The paper develops a standardized data model for cancer genomics to enable interoperable data sharing across countries.

9 citations

whole-genome sequencingbenchmark study

𝕏 PostBluesky

BioinformaticsBioinformaticsPeer-reviewedApr 30, 2024

Scalable Integration of Multiomic Single Cell Data Using Generative Adversarial Networks

Valentina Giansanti, Francesca Giannese, Oronza Antonietta Botrugno…·Davide Cittaro

This paper introduces a framework for integrating multiomic single-cell data using Wasserstein GANs, enabling analysis of more than two modalities with reduced computational burden.

6 citations

scRNA-seqATAC-seq

𝕏 PostBluesky