Mathias Wilhelm

发表

A Classifier Based on Accurate Mass Measurements to Aid Large Scale, Unbiased Glycoproteomics*

Eric D. Dodds, Richard S. Lee, John W. Froehlich, 2013, Molecular & Cellular Proteomics.

Mass-spectrometry-based draft of the human proteome

B. Kuster, Mathias Wilhelm, M. Bantscheff, 2014, Nature.

The target landscape of clinical kinase drugs

Heiner Koch, B. Kuster, R. Preissner, 2017, Science.

Challenges in Clinical Metaproteomics Highlighted by the Analysis of Acute Leukemia Patients with Gut Colonization by Multidrug-Resistant Enterobacteriaceae

B. Kuster, Mathias Wilhelm, J. Behr, 2019, Proteomes.

Mass-spectrometry-based draft of the Arabidopsis proteome

Mathias Wilhelm, Jan Baumbach, Claus Schwechheimer, 2020, Nature.

Proteome activity landscapes of tumor cell lines determine drug responses

B. Kuster, Mathias Wilhelm, W. Weichert, 2020, Nature Communications.

ProteomeTools: Systematic Characterization of 21 Post-translational Protein Modifications by Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) Using Synthetic Peptides*

Mathias Wilhelm, Ulf Reimer, Bernhard Kuster, 2018, Molecular & Cellular Proteomics.

An in-memory platform for the exploration and analysis of big data in biology

Mathias Wilhelm, 2017 .

Maltcms an Application Framework for Processing of Metabolomics{Data

Jens Stoye, Mathias Wilhelm, Karsten Niehaus, 2009 .

Generating high quality libraries for DIA MS with empirically corrected peptide predictions

Kristian E. Swearingen, B. Searle, Mathias Wilhelm, 2020, Nature Communications.

Wilhelm et al. reply

Mathias Wilhelm, Hannes Hahne, Marcus Bantscheff, 2003, Nature.

Meltome atlas—thermal proteome stability across the tree of life

Thomas A. Hopf, B. Kuster, Mathias Wilhelm, 2020, Nature Methods.

Generating high quality libraries for DIA MS with empirically corrected peptide predictions

Mathias Wilhelm, Brian C. Searle, Bernhard Kuster, 2019, Nature Communications.

A deep proteome and transcriptome abundance atlas of 29 healthy human tissues

Thomas A. Hopf, B. Kuster, T. Wieland, 2018, bioRxiv.

Trimodal Mixed Mode Chromatography That Enables Efficient Offline Two-Dimensional Peptide Fractionation for Proteome Analysis.

B. Kuster, Mathias Wilhelm, S. Petzoldt, 2017, Analytical chemistry.

Ethylene glycol improves electrospray ionization efficiency in bottom-up proteomics

B. Kuster, Mathias Wilhelm, Hannes Hahne, 2017, Analytical and Bioanalytical Chemistry.

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

Aubry K. Miller, Raphael R. Steimbach, B. Kuster, 2022, Nature Chemical Biology.

A proteomics sample metadata representation for multiomics integration and big data analysis

A. Brazma, Mathias Wilhelm, E. Deutsch, 2021, Nature Communications.

Phosphoproteome Profiling Reveals Molecular Mechanisms of Growth-Factor-Mediated Kinase Inhibitor Resistance in EGFR-Overexpressing Cancer Cells.

Heiner Koch, B. Kuster, Mathias Wilhelm, 2016, Journal of proteome research.

Mass spectrometry-based draft of the mouse proteome

Rashmi R. Hazarika, Florian P Bayer, B. Kuster, 2022, Nature Methods.

A proteomics sample metadata representation for multiomics integration and big data analysis

A. Brazma, Mathias Wilhelm, E. Deutsch, 2021, Nature Communications.

Semi-supervised Learning Predicts Approximately One Third of the Alternative Splicing Isoforms as Functional Proteins Graphical

Philip M. Kim, B. Kuster, A. Ignatchenko, 2016 .

Peptide Level Turnover Measurements Enable the Study of Proteoform Dynamics *

B. Kuster, Mathias Wilhelm, C. Meng, 2018, Molecular & Cellular Proteomics.

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

B. Kuster, Mathias Wilhelm, M. The, 2022, Molecular & cellular proteomics : MCP.

Mass-spectrometry-based draft of the Arabidopsis proteome 1

Rashmi R. Hazarika, Ajeet, Klaus F. X. Mayer, 2020 .

ProteomeTools: Systematic characterization of 21 post-translational protein modifications by LC-MS/MS using synthetic peptides

Mathias Wilhelm, K. Schnatbaum, U. Reimer, 2018 .

Proteogenomic analysis reveals RNA as an important source for tumor-agnostic neoantigen identification correlating with T-cell infiltration

Florian S. Dreyer, S. Fröhling, B. Kuster, 2022, bioRxiv.

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

B. Kuster, Mathias Wilhelm, K. Schnatbaum, 2022, Analytical chemistry.

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

Mathias Wilhelm, Ludwig Lautenbacher, Wassim Gabriel, 2022, Proteomics.

Ion Mobility Tandem Mass Spectrometry Enhances Performance of Bottom-up Proteomics

Mathias Wilhelm, Bernhard Kuster, Johannes P C Vissers, 2014, Molecular & Cellular Proteomics.

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

B. Kuster, Mathias Wilhelm, Patroklos Samaras, 2022, Molecular & cellular proteomics : MCP.

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

Mathias Wilhelm, J. Gagneur, Daniela Klaproth-Andrade, 2023, bioRxiv.

Pharmacoproteomic characterisation of human colon and rectal cancer

Heiner Koch, B. Kuster, Mathias Wilhelm, 2017, Molecular systems biology.

ProteomicsDB

Helmut Krcmar, Mathias Wilhelm, Harald Kienegger, 2017, Nucleic Acids Res..

ProteomicsDB: toward a FAIR open-source resource for life-science research

B. Rost, Mathias Wilhelm, H. Krcmar, 2021, Nucleic Acids Res..

ProteomicsDB: a multi-omics and multi-organism resource for life science research

Helmut Krcmar, Mathias Wilhelm, Harald Kienegger, 2019, Nucleic Acids Res..

Real-time spectral library matching for sample multiplexed quantitative proteomics

Devin K. Schweppe, Mathias Wilhelm, J. Canterbury, 2023, bioRxiv.

A deep proteome and transcriptome abundance atlas of 29 healthy human tissues

Mathias Wilhelm, Thomas A. Hopf, Mathias Uhlen, 2018, bioRxiv.

Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

S. Carr, B. Kuster, Mathias Wilhelm, 2021, Nature Communications.

Proteome activity landscapes of tumor cell lines determine drug responses

B. Kuster, Mathias Wilhelm, W. Weichert, 2020, Nature Communications.

Global proteome analysis of the NCI-60 cell line panel.

Mathias Wilhelm, Bernhard Kuster, Amin Moghaddas Gholami, 2013, Cell reports.

Optimized chemical proteomics assay for kinase inhibitor profiling.

B. Kuster, Mathias Wilhelm, Susan Klaeger, 2015, Journal of proteome research.

Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning

Mathias Wilhelm, Stephan Aiche, Ulf Reimer, 2019, Nature Methods.

Toward an Integrated Machine Learning Model of a Proteomics Experiment

Benjamin A. Neely, Samuel H. Payne, S. Degroeve, 2023, Journal of proteome research.

Linking post-translational modifications and protein turnover by site-resolved protein turnover profiling

B. Kuster, Mathias Wilhelm, F. Bassermann, 2022 .

A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets

Mathias Wilhelm, Bernhard Kuster, Marcus Bantscheff, 2015, Molecular & Cellular Proteomics.

Building ProteomeTools based on a complete synthetic human proteome

Mathias Wilhelm, Ruedi Aebersold, Stephan Aiche, 2017, Nature Methods.

Meltome atlas—thermal proteome stability across the tree of life

Thomas A. Hopf, B. Kuster, Mathias Wilhelm, 2020, Nature Methods.

Kinobeads: A Chemical Proteomic Approach for Kinase Inhibitor Selectivity Profiling and Target Discovery

B. Kuster, Mathias Wilhelm, Susan Klaeger, 2019, Methods and Principles in Medicinal Chemistry.

Chemoproteomic Selectivity Profiling of PIKK and PI3K Kinase Inhibitors.

B. Kuster, Mathias Wilhelm, S. Heinzlmeir, 2019, ACS chemical biology.

CiRCus: A Framework to Enable Classification of Complex High-Throughput Experiments.

Mathias Wilhelm, Bernhard Kuster, Tobias Schmidt, 2019, Journal of proteome research.

Chemoproteomics‐Aided Medicinal Chemistry for the Discovery of EPHA2 Inhibitors

B. Kuster, K. Saxena, H. Schwalbe, 2017, ChemMedChem.

Chemical Proteomics and Structural Biology Define EPHA2 Inhibition by Clinical Kinase Drugs.

B. Kuster, K. Saxena, H. Schwalbe, 2016, ACS chemical biology.

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

Aubry K. Miller, Raphael R. Steimbach, B. Kuster, 2022, Nature Chemical Biology.

Expanding the Use of Spectral Libraries in Proteomics.

Johannes Griss, Yasset Perez-Riverol, Juan Antonio Vizcaíno, 2018, Journal of proteome research.

Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning

B. Kuster, Mathias Wilhelm, Siegfried Gessulat, 2019, Nature Methods.

Generating high quality libraries for DIA MS with empirically corrected peptide predictions

Kristian E. Swearingen, B. Searle, Mathias Wilhelm, 2020, Nature Communications.

Semi-supervised Learning Predicts Approximately One Third of the Alternative Splicing Isoforms as Functional Proteins.

Mathias Wilhelm, Joan Teyra, Bernhard Kuster, 2015, Cell reports.

Identification of 7 000-9 000 Proteins from Cell Lines and Tissues by Single-Shot Microflow LC-MS/MS.

B. Kuster, Mathias Wilhelm, P. Giansanti, 2021, Analytical chemistry.

Universal Spectrum Explorer: A Standalone (Web-)Application for Cross-Resource Spectrum Comparison.

B. Kuster, Mathias Wilhelm, Christian Panse, 2021, Journal of proteome research.

The emerging landscape of single-molecule protein sequencing technologies

B. Kuster, P. Yin, E. Marcotte, 2021, Nature Methods.

Mining the Human Tissue Proteome for Protein Citrullination *

B. Kuster, Mathias Wilhelm, F. Pontén, 2018, Molecular & Cellular Proteomics.

mz5: Space- and Time-efficient Storage of Mass Spectrometry Data Sets*

Mathias Wilhelm, Marc Kirchner, Judith A J Steen, 2011, Molecular & Cellular Proteomics.

PROTEOFORMER 2.0: Further Developments in the Ribosome Profiling-assisted Proteogenomic Hunt for New Proteoforms*

Mathias Wilhelm, Gerben Menschaert, Bernhard Kuster, 2019, Molecular & Cellular Proteomics.

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

Thomas A. Hopf, Florian P Bayer, B. Kuster, 2023, Science.

Proteomic and transcriptomic profiling of aerial organ development in Arabidopsis

Klaus F. X. Mayer, B. Kuster, Mathias Wilhelm, 2020, Scientific Data.

Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets

Jens Stoye, Mathias Wilhelm, Karsten Niehaus, 2012, BMC Bioinformatics.

Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics

S. Degroeve, B. Kuster, Mathias Wilhelm, 2021, Molecular & cellular proteomics : MCP.

PROCAL: A Set of 40 Peptide Standards for Retention Time Indexing, Column Performance Monitoring, and Collision Energy Calibration

Mathias Wilhelm, Ulf Reimer, Bernhard Kuster, 2017, Proteomics.

Universal Spectrum Explorer: A standalone (web-)application for cross-resource spectrum comparison

Yasset Perez-Riverol, Eric W. Deutsch, Christian Panse, 2020, bioRxiv.

Prosit Transformer: A transformer for Prediction of MS2 Spectrum Intensities

Mathias Wilhelm, L. Käll, Wassim Gabriel, 2022, Journal of proteome research.

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

B. Kuster, Mathias Wilhelm, Patroklos Samaras, 2022, Cell reports.

Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics

William Stafford Noble, S. Degroeve, Mathias Wilhelm, 2022, Journal of proteome research.

HPI Future SOC Lab – Proceedings 2017

Andrew M. Sutton, F. M. Soares, Estée van der Walt, 2020 .

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

Florian S. Dreyer, S. Fröhling, B. Kuster, 2023, Nature communications.

PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics

Mathias Wilhelm, Omar Shouman, Wassim Gabriel, 2022, NeurIPS.

Proteomic and transcriptomic profiling of aerial organ development in Arabidopsis

Klaus F. X. Mayer, B. Kuster, Mathias Wilhelm, 2020, Scientific Data.

PROTEOFORMER 2.0: Further Developments in the Ribosome Profiling-assisted Proteogenomic Hunt for New Proteoforms

B. Kuster, Mathias Wilhelm, Siegfried Gessulat, 2019, Molecular & Cellular Proteomics.

Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF

Mathias Wilhelm, K. Laukens, K. Boonen, 2024, bioRxiv.

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

Mathias Wilhelm, Ludwig Lautenbacher, Firas Hamood, 2023, Proteomics.

Linking post-translational modifications and protein turnover by site-resolved protein turnover profiling

B. Kuster, Mathias Wilhelm, F. Bassermann, 2022, Nature Communications.

ProteomeTools: Systematic characterization of 21 post-translational protein modifications by LC-MS/MS using synthetic peptides

Mathias Wilhelm, K. Schnatbaum, U. Reimer, 2018 .

Semi-supervised Learning Predicts Approximately One Third of the Alternative Splicing Isoforms as Functional Proteins.

Philip M. Kim, B. Kuster, A. Ignatchenko, 2015, Cell reports.

The target landscape of clinical kinase drugs

B. Kuster, R. Preissner, Mathias Wilhelm, 2017, Science.

PTMNavigator: Interactive Visualization of Differentially Regulated Post-Translational Modifications in Cellular Signaling Pathways

Mathias Wilhelm, Florian P. Bayer, Julian Müller, 2023, bioRxiv.

PROCAL: A Set of 40 Peptide Standards for Retention Time Indexing, Column Performance Monitoring, and Collision Energy Calibration

B. Kuster, Mathias Wilhelm, K. Schnatbaum, 2017, Proteomics.

BiPACE 2D - graph-based multiple alignment for comprehensive 2D gas chromatography-mass spectrometry

Jens Stoye, Mathias Wilhelm, Karsten Niehaus, 2014, Bioinform..

Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

Mathias Wilhelm, K. Schnatbaum, Susan Klaeger, 2021, Nature Communications.