Mathias Wilhelm

11 papers in the catalog · Germany

Mathias Wilhelm: 11 papers in the catalog · Germany · Technical University of Munich, Technical University of Munich · Works on Living proteomics benchmark, Modanovo, Pairwise

Affiliations

Technical University of Munich · Chair of Proteomics and Bioanalytics
Technical University of Munich · Computational Mass Spectrometry, School of Life Sciences
Technical University of Munich · Computational Mass Spectrometry, TUM School of Life Sciences
Technical University of Munich · Munich Data Science Institute
Technical University of Munich · TUM School of Life Sciences

Elsewhere

ORCID 0000-0002-9224-3258 · OpenAlex

Papers

PepPr: De Novo peptide sequencing with peptide priors (2026, bioRxiv)
dIon: Fragmentation-Based Invariance for Self-Supervised Learning of Tandem Mass Spectra (2026, arXiv)
Beyond Best Guesses: A Multi-Level Confidence Assessment Framework for De Novo Peptide Sequencing Results (2026, HUPO 2026 World Congress)
A living proteomics benchmark for comprehensive evaluation of deep learning-based de novo peptide sequencing tools (2026, Nature Methods (Registered Report))
Modanovo: A Unified Model for Post-translational Modification-Aware De Novo Sequencing Using Experimental Spectra From In Vivo and Synthetic Peptides (2025, Molecular & Cellular Proteomics)
Modanovo: A Unified Model for Post-Translational Modification-Aware de Novo Sequencing Using Experimental Spectra from In Vivo and Synthetic Peptides (2025, bioRxiv)
Pairwise Attention: Leveraging Mass Differences to Enhance De Novo Sequencing of Mass Spectra (2025, Journal of Proteome Research)
Pairwise Attention: Leveraging Mass Differences to Enhance De Novo Sequencing of Mass Spectra (2025, bioRxiv)
Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing (2024, Nature Communications)
Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing (2023, bioRxiv)
Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning (2019, Nature Methods)

Methods and tools

Living proteomics benchmark, Modanovo, Pairwise, PepPr, Peptide similarity alignment, Prosit, Spectralis, dIon

Co-authors (77)

Ranked by Newman fractional collaboration strength, so a pair on a two-author paper counts for more than a pair on a 50-author consortium paper.

Alfred Nilsson strength 0.94, 4 shared
Joel Lapin strength 0.94, 4 shared
Lukas Käll strength 0.94, 4 shared
Daniela Klaproth-Andrade strength 0.92, 6 shared
Julien Gagneur strength 0.92, 6 shared
Yanik Bruns strength 0.70, 4 shared
Armin Suleymaniniya strength 0.50, 1 shared
Cemil Can Saylan strength 0.50, 1 shared
Jakob Träuble strength 0.37, 2 shared
Johannes Hingerl strength 0.37, 2 shared
Nicholas H. Smith strength 0.37, 2 shared
Christian Nix strength 0.30, 3 shared
Andreas Pichlmair strength 0.29, 2 shared
Valter Bergant strength 0.29, 2 shared
Wassim Gabriel strength 0.29, 2 shared
Samuel H. Payne strength 0.25, 1 shared
Samuel Khan strength 0.25, 1 shared
Andreas Huhmer strength 0.07, 1 shared
Bernard Delanghe strength 0.07, 1 shared
Bernhard Kuster strength 0.07, 1 shared
Daniel Paul Zolg strength 0.07, 1 shared
Hans-Christian Ehrlich strength 0.07, 1 shared
Johannes Zerweck strength 0.07, 1 shared
Julia Rechenberger strength 0.07, 1 shared
Karsten Schnatbaum strength 0.07, 1 shared
Patroklos Samaras strength 0.07, 1 shared
Siegfried Gessulat strength 0.07, 1 shared
Stephan Aiche strength 0.07, 1 shared
Tobias Knaute strength 0.07, 1 shared
Tobias Schmidt strength 0.07, 1 shared
Ulf Reimer strength 0.07, 1 shared
Baozhen Shan strength 0.02, 1 shared
Benjamin A. Neely strength 0.02, 1 shared
Bin Ma strength 0.02, 1 shared
Ceder Dens strength 0.02, 1 shared
Charlotte Adams strength 0.02, 1 shared
Cheng Chang strength 0.02, 1 shared
Cheng Ge strength 0.02, 1 shared
Chenyu Yao strength 0.02, 1 shared
Di Zhang strength 0.02, 1 shared

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