M
Mathias Wilhelm
Researcher at Technische Universität München
Publications - 75
Citations - 6116
Mathias Wilhelm is an academic researcher from Technische Universität München. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 25, co-authored 54 publications receiving 4128 citations. Previous affiliations of Mathias Wilhelm include Bielefeld University & Boston Children's Hospital.
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Journal ArticleDOI
Mass-spectrometry-based draft of the human proteome
Mathias Wilhelm,Judith Schlegl,Hannes Hahne,Amin Moghaddas Gholami,Marcus Lieberenz,Mikhail M. Savitski,Emanuel Ziegler,Lars Butzmann,Siegfried Gessulat,Harald Marx,Toby Mathieson,Simone Lemeer,Karsten Schnatbaum,Ulf Reimer,Holger Wenschuh,Martin Mollenhauer,Julia Slotta-Huspenina,Joos-Hendrik Boese,Marcus Bantscheff,Anja Gerstmair,Franz Faerber,Bernhard Kuster +21 more
TL;DR: A mass-spectrometry-based draft of the human proteome and a public, high-performance, in-memory database for real-time analysis of terabytes of big data, called ProteomicsDB are presented, which enables navigation of proteomes, provides biological insight and fosters the development of proteomic technology.
Journal ArticleDOI
The target landscape of clinical kinase drugs
Susan Klaeger,Susan Klaeger,Stephanie Heinzlmeir,Stephanie Heinzlmeir,Mathias Wilhelm,Harald Polzer,Harald Polzer,Binje Vick,Paul-Albert Koenig,Maria Reinecke,Maria Reinecke,Benjamin Ruprecht,Svenja Petzoldt,Svenja Petzoldt,Chen Meng,Jana Zecha,Jana Zecha,Katrin Reiter,Katrin Reiter,Huichao Qiao,Dominic Helm,Heiner Koch,Heiner Koch,Melanie Schoof,G. Canevari,Elena Casale,Stefania Re Depaolini,Annette Feuchtinger,Zhixiang Wu,Tobias Schmidt,Lars Rueckert,Wilhelm Becker,Jan Huenges,Anne-Kathrin Garz,Bjoern-Oliver Gohlke,Bjoern-Oliver Gohlke,Daniel P Zolg,Gian Kayser,Tõnu Vooder,Tõnu Vooder,Robert Preissner,Robert Preissner,Hannes Hahne,Neeme Tõnisson,Neeme Tõnisson,Karl Kramer,Katharina Götze,Florian Bassermann,Judith Schlegl,Hans-Christian Ehrlich,Stephan Aiche,Axel Walch,Philipp A. Greif,Philipp A. Greif,Sabine Schneider,Eduard R. Felder,Juergen Ruland,Guillaume Médard,Irmela Jeremias,Karsten Spiekermann,Karsten Spiekermann,Bernhard Kuster +61 more
TL;DR: A comprehensive analysis of 243 kinase inhibitors that are either approved for use or in clinical trials provides an open-access resource of target summaries that could help researchers develop better drugs, understand how existing drugs work, and design more effective clinical trials.
Journal ArticleDOI
Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.
Siegfried Gessulat,Tobias Schmidt,Daniel P Zolg,Patroklos Samaras,Karsten Schnatbaum,Johannes Zerweck,Tobias Knaute,Julia Rechenberger,Bernard Delanghe,Andreas Huhmer,Ulf Reimer,Hans-Christian Ehrlich,Stephan Aiche,Bernhard Kuster,Mathias Wilhelm +14 more
TL;DR: A deep learning–based tool, Prosit, predicts high-quality peptide tandem mass spectra, improving peptide-identification performance compared with that of traditional proteomics analysis methods.
Journal ArticleDOI
A deep proteome and transcriptome abundance atlas of 29 healthy human tissues
Dongxue Wang,Basak Eraslan,Basak Eraslan,Thomas Wieland,Björn M. Hallström,Thomas A. Hopf,Daniel P Zolg,Jana Zecha,Anna Asplund,Li-hua Li,Chen Meng,Martin Frejno,Tobias Schmidt,Karsten Schnatbaum,Mathias Wilhelm,Fredrik Pontén,Mathias Uhlén,Julien Gagneur,Hannes Hahne,Bernhard Kuster,Bernhard Kuster +20 more
TL;DR: A quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNAs and that protein expression is often more stable across tissues than that of transcripts.
Journal ArticleDOI
A scalable approach for protein false discovery rate estimation in large proteomic data sets
Mikhail M. Savitski,Mathias Wilhelm,Hannes Hahne,Bernhard Kuster,Bernhard Kuster,Marcus Bantscheff +5 more
TL;DR: This study investigates the merits of the classic, as well as a novel target–decoy-based protein FDR estimation approach, taking advantage of a heterogeneous data collection comprised of ∼19,000 LC-MS/MS runs deposited in ProteomicsDB.