Author

Vincent Procaccio

Centre National de la Recherche ScientifiqueORCID 0000-0002-1537-4684Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:DNA methylation· 2 Approach:machine learning (classical)· 2 Research Focus:variant effect prediction· 2 Assay:whole-genome sequencing· 2 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Agnese GiovannettiiD· 2 shared Alessandro NapoliiD· 2 shared Douglas C. WallaceiD· 2 shared Francesco PetrizzelliiD· 2 shared Marie T. LottiD· 2 shared Niccolò LiorniiD· 2 shared Salvatore Daniele BiancoiD· 2 shared Shiping ZhangiD· 2 shared

Activity by Year

2023-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Agnese GiovannettiORCID

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintDec 23, 2025

nAPOGEE: A machine-learning platform for clinically actionable pathogenicity assessment of all mitochondrial noncoding variants

Salvatore Daniele Bianco, Agnese Giovannetti, Marta Adinolfi…·Tommaso Mazza

nAPOGEE assesses pathogenicity of mitochondrial noncoding variants using machine learning, revealing consistent spatial correlation of predicted pathogenicity.

2 citations

humanDNA methylation

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Genetic & Genomic MedicinemedRxivPreprintOct 23, 2023

Exploring global and specific pathogenic mechanisms in Chronic Chagas Cardiomyopathy through multi-omics integration

Pauline Brochet, Jorge Elias Kalil, Vincent Procaccio…·Christophe Chevillard

This study identifies two distinct pathogenic pathways in Chronic Chagas Cardiomyopathy through multi-omics integration, involving IRF4 and TLR signaling, with B cell-related genes and shared B cell receptor features.

DOI 10.1101/2023.10.23.23297068

humanbulk RNA-seq

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BioinformaticsNature CommunicationsPeer-reviewedAug 19, 2023

APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants

Salvatore Daniele Bianco, Luca Parca, Francesco Petrizzelli…·Tommaso Mazza

APOGEE 2 improves pathogenicity prediction for mitochondrial missense variants using a machine learning model based on ACMG/AMP guidelines and provides region-wise genome fragility assessments.

44 citationstop 2%

humanwhole-genome sequencing

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