Author

Nicolò Alagna

University Medical Center of the Johannes Gutenberg University MainzORCID 0009-0006-0804-5774Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Biophysics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Assay:long-read sequencing· 2 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Organism:human· 1 Organism:mouse· 1

Frequent Co-Authors

Susanne GerberiD· 3 shared Anna WierczeikoiD· 2 shared Johannes MiedemaiD· 2 shared Lioba Lehmann· 2 shared Mark HelmiD· 2 shared Stefan Pastore· 2 shared Tamer ButtoiD· 2 shared Alexander Haehnel· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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3 shared papersBioinformatics

Susanne GerberORCID

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Author profile →

3 papers

Recent·Most cited

DModE: An end-to-end framework for Differential Modification and Expression Analysis of Nanopore direct RNA sequencing data

Johannes Miedema, Stefan Pastore, Luca Drescher, Alexander Haehnel, Anna Wierczeiko, Nicolò Alagna, Lioba Lehmann, Mark Helm, Tamer Butto, Susanne Gerber

DModE provides an end-to-end framework for analyzing Nanopore DRS data, enabling integrated differential expression and modification analysis with automated reporting.

bioRxiv · Jun 11, 2026 · PreprintBioinformatics

ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing

Nicolò Alagna, Stefan Mündnich, Johannes Miedema, Stefan Pastore, Lioba Lehmann, Anna Wierczeiko, Johannes Friedrich, Lukas Walz, Marko Jörg, Tamer Butto, Kristina Friedland, Mark Helm, Susanne Gerber

ModiDeC is a deep-learning classifier that identifies multiple RNA modifications from direct nanopore sequencing, validated on synthetic data and biological samples.

Nucleic Acids Research · Jul 19, 2025Peer-reviewedBioinformatics

Deep Learning Reaction Network: a machine learning framework for modeling time resolved data

Nicolò Alagna, Brigitta Dúzs, Vincent Dietrich, Ali Tayefeh Younesi, Livia Lehmann, Ronald Ulbricht, Heinz Koeppl, Andreas Walther, Susanne Gerber

The paper introduces DLRN, a deep learning framework that models time-resolved data to extract kinetic reaction networks, time constants, and amplitudes with performance comparable to or better than classical methods.

Communications Chemistry · May 15, 2025Peer-reviewedBiophysics