Author

Luca Ponzoni

Scuola Internazionale Superiore di Studi AvanzatiORCID 0000-0001-8125-582XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Assay:protein sequence· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Assay:metagenomics· 1

Frequent Co-Authors

Alexandre Victor FassioiD· 1 shared Cristian MichelettiiD· 1 shared Daniel Peñaherrera· 1 shared Daniele GranataiD· 1 shared İvet BahariD· 1 shared Jessica L. McKinleyiD· 1 shared Laura ShubiD· 1 shared Matthew J. O’MearaiD· 1 shared

Activity by Year

2017-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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1 shared paperBioinformatics

Alexandre Victor FassioORCID

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3 papers

Recent·Most cited

BioinformaticsJournal of Chemical Information and ModelingPeer-reviewedSep 14, 2022

Prioritizing virtual screening with interpretable interaction fingerprints

Alexandre Victor Fassio, Laura Shub, Luca Ponzoni…·Raquel C. de Melo-Minardi

The paper introduces LUNA, a toolkit that generates interpretable interaction fingerprints for protein-ligand interactions to improve machine learning-based virtual screening.

57 citationstop 2%

proteomicsmetagenomics

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BioinformaticsBioinformaticsPeer-reviewedFeb 21, 2020

Rhapsody: Pathogenicity prediction of human missense variants based on protein sequence, structure and dynamics

Luca Ponzoni, Daniel Peñaherrera, Zoltán N. Oltvai, İvet Bahar

The paper introduces Rhapsody, a tool that predicts the pathogenicity of human missense variants by integrating sequence, structure, and dynamics data, outperforming existing methods.

101 citationstop 1%

humanprotein sequence

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BioinformaticsProceedings of the National Academy of SciencesPeer-reviewedNov 28, 2017

From Sequence to Function: Coevolving Amino Acids Encode Structural and Functional Domains

Daniele Granata, Luca Ponzoni, Cristian Micheletti, Vincenzo Carnevale

The paper introduces a co-evolutionary coupling analysis to identify structural and functional domains from protein sequences, showing that sequence-based methods can recover domain characteristics.

35 citations

protein sequencestatistical model

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