Mathias Wilhelm

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Technical University of Munich

Germany

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About

Mathias Wilhelm is a Professor at the Technical University of Munich, Germany. His research focuses on advanced methodologies in mass spectrometry and proteomics, with recent articles addressing topics such as peptide detectability prediction using deep learning, multi-omics integration, and post-translational modification visualization. He is actively contributing to the field through innovative frameworks and models aimed at enhancing molecular analysis and proteomic diversity.

Recent Grants

Grant: Close

European Proteomics Infrastructure Consortium providing Access

Open Date: 2019-01-01

Close Date: 2023-06-30

Articles (26)

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Collaborators (21)

Wassim Gabriel

Technical University of Munich

GERMANY

Mathias Wilhelm

Research Interests

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About

Recent Grants

Articles (26)

Unifying the analysis of bottom-up proteomics data with CHIMERYS

To Fly, or Not to Fly, That Is the Question

Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.

Integration of multi-omics data accelerates molecular analysis of common wheat traits.

ProSIMSIt: The Best of Both Worlds in Data-Driven Rescoring and Identification Transfer.

SWAPS: A Modular Deep-Learning Empowered Peptide Identity Propagation Framework Beyond Match-Between-Run.

Deep Learning Enhances Precision of Citrullination Identification in Human and Plant Tissue Proteomes.

Author Correction: π-HuB: the proteomic navigator of the human body.

PTMNavigator: interactive visualization of differentially regulated post-translational modifications in cellular signaling pathways.

Proteomic diversity in bacteria: Insights and implications for bacterial identification.

Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing

Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification.

Toward an Integrated Machine Learning Model of a Proteomics Experiment

Decrypting drug actions and protein modifications by dose- and time-resolved proteomics.

Assessment and Prediction of Human Proteotypic Peptide Stability for Proteomics Quantification

Chemical proteomics reveals the target landscape of 1,000 kinase inhibitors

Oktoberfest: Open‐source spectral library generation and rescoring pipeline based on Prosit

The Proteomes that Feed the World

Reanalysis of ProteomicsDB Using an Accurate, Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups.

Mass spectrometry-based draft of the mouse proteome.

Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides.

SIMSI-Transfer: Software-Assisted Reduction of Missing Values in Phosphoproteomic and Proteomic Isobaric Labeling Data Using Tandem Mass Spectrum Clustering.

High temporal resolution proteome and phosphoproteome profiling of stem cell-derived hepatocyte development

Prosit Transformer: A transformer for Prediction of MS2 Spectrum Intensities

Predicting fragment intensities and retention time of iTRAQ‐ and TMTPro‐labeled peptides with Prosit‐TMT

Target deconvolution of HDAC pharmacopoeia reveals MBLAC2 as common off-target.

Collaborators (21)

Wassim Gabriel

Naim Abdul Khalek

Magnus Palmblad

Bernhard Kuster

Christian Grätz

Guillaume Médard

Eduard Sabidó

Sven Degroeve

Daniel Paul Zolg

Lennart Martens

Michael Pfaffl

Matthew The

Simon Gregersen Echers

Viktoria Dorfer

Barbara Treutlein

Jürgen Ruland

Mar Abril-Gil

Patrick Leopold Rüther

Sebastian Bauer

Veit Schwämmle

Lukas Käll

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