| Mass-spectrometry-based draft of the human proteome M Wilhelm, J Schlegl, H Hahne, AM Gholami, M Lieberenz, MM Savitski, ... Nature 509 (7502), 582-587, 2014 | 2145 | 2014 |
| Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning S Gessulat, T Schmidt, DP Zolg, P Samaras, K Schnatbaum, J Zerweck, ... Nature methods 16 (6), 509-518, 2019 | 773 | 2019 |
| Building ProteomeTools based on a complete synthetic human proteome DP Zolg, M Wilhelm, K Schnatbaum, J Zerweck, T Knaute, B Delanghe, ... Nature methods 14 (3), 259-262, 2017 | 246 | 2017 |
| Generating high quality libraries for DIA MS with empirically corrected peptide predictions BC Searle, KE Swearingen, CA Barnes, T Schmidt, S Gessulat, B Küster, ... Nature communications 11 (1), 1548, 2020 | 236 | 2020 |
| ProteomicsDB T Schmidt, P Samaras, M Frejno, S Gessulat, M Barnert, H Kienegger, ... Nucleic acids research 46 (D1), D1271-D1281, 2018 | 234 | 2018 |
| ProteomicsDB: a multi-omics and multi-organism resource for life science research P Samaras, T Schmidt, M Frejno, S Gessulat, M Reinecke, A Jarzab, ... Nucleic acids research 48 (D1), D1153-D1163, 2020 | 198 | 2020 |
| Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics M Wilhelm, DP Zolg, M Graber, S Gessulat, T Schmidt, K Schnatbaum, ... Nature communications 12 (1), 3346, 2021 | 152 | 2021 |
| INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results DP Zolg, S Gessulat, C Paschke, M Graber, M Rathke‐Kuhnert, F Seefried, ... Rapid Communications in Mass Spectrometry, e9128, 2021 | 59 | 2021 |
| PROTEOFORMER 2.0: further developments in the ribosome profiling-assisted proteogenomic hunt for new proteoforms S Verbruggen, E Ndah, W Van Criekinge, S Gessulat, B Kuster, ... Molecular & Cellular Proteomics 18 (8), S126-S140, 2019 | 49 | 2019 |
| Spectral prediction features as a solution for the search space size problem in proteogenomics S Verbruggen, S Gessulat, R Gabriels, A Matsaroki, H Van de Voorde, ... Molecular & Cellular Proteomics 20, 2021 | 40 | 2021 |
| Toward an integrated machine learning model of a proteomics experiment BA Neely, V Dorfer, L Martens, I Bludau, R Bouwmeester, S Degroeve, ... Journal of proteome research 22 (3), 681-696, 2023 | 35 | 2023 |
| ProteomicsML: an online platform for community-curated data sets and tutorials for machine learning in proteomics TG Rehfeldt, R Gabriels, R Bouwmeester, S Gessulat, BA Neely, ... Journal of proteome research 22 (2), 632-636, 2023 | 14 | 2023 |
| CHIMERYS: An AI-Driven Leap Forward in Peptide Identification M Frejno, DP Zolg, T Schmidt, S Gessulat, M Graber, F Seefried, ... the 69th ASMS Conference on Mass Spectrometry and Allied Topics, 2021 | 8 | 2021 |
| Unifying the analysis of bottom-up proteomics data with CHIMERYS M Frejno, MT Berger, J Tueshaus, A Hogrebe, F Seefried, M Graber, ... bioRxiv, 2024.05. 27.596040, 2024 | 3 | 2024 |
| A review of real-time models for transportation mode detection S Gessulat Free University of Berlin, Tech. Rep, 2013 | 3 | 2013 |
| A deep learning model for the proteome-wide prediction of peptide tandem mass spectra S Gessulat Technische Universität München, 2020 | 1 | 2020 |
| An AI-driven leap forward in peptide identification through the deconvolution of chimeric spectra M Frejno, DP Zolg, T Schmidt, S Gessulat, M Graber, F Seefried, ... MOLECULAR & CELLULAR PROTEOMICS 21 (8), S40-S40, 2022 | | 2022 |
| User interface for clinical measures analytics M Krauss, M Steinbrecher, S Gessulat, JM Pilzer US Patent App. 15/167,296, 2016 | | 2016 |
| A unifying, spectrum-centric approach for the analysis of peptide tandem mass spectra DP Zolg, F Seefried, T Schmidt, S Gessulat, M Graber, M Rathke-Kuhnert, ... | | |
| Digging deeper into phosphoproteomes through AI-driven deconvolution of chimeric spectra F Seefried, S Gessulat, M Graber, V Sukumar, SB Fredj, P Samaras, ... | | |
Mathias WilhelmComputational Mass Spectrometry, Technical University of MunichVerified email at tum.de
