Author

Marco Scutari

Dalle Molle Institute for Artificial Intelligence ResearchORCID 0000-0002-2151-7266Unclaimed 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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Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:gene regulatory network inference· 1 Assay:GWAS summary statistics· 1 Approach:machine learning (classical)· 1 Assay:scRNA-seq· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Daniele MalpettiiD· 1 shared Francesca MangiliiD· 1 shared Francesco GualdiiD· 1 shared Noriaki SatoiD· 1 shared Seiya ImotoiD· 1 shared Zeno Darani· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 7 mutual co-author links

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1 shared paperBioinformatics

Daniele MalpettiORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintOct 26, 2026

A Pipeline for Predicting Variant Associations with Complex Disorders via Functional Annotations

Francesco Gualdi, Zeno Darani, Daniele Malpetti…·Francesca Mangili

The paper introduces a pipeline to create curated, balanced variant-trait association datasets for complex disorders using GWAS and functional annotations.

DOI 10.64898/2026.10.21.760618

GWAS summary statisticsstatistical model

𝕏 PostBluesky

BioinformaticsbioRxivPreprintDec 19, 2025

Causal assessment of gene regulatory network in single-cell transcriptomics data based on Bayesian networks

Noriaki Sato, Marco Scutari, Seiya Imoto

The paper introduces scstruc, an R package for learning and evaluating Bayesian networks from single-cell transcriptomics data to infer causal gene regulatory relationships.

DOI 10.64898/2025.12.17.695014

scRNA-seqstatistical model

𝕏 PostBluesky