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Siegfried Gessulat
Siegfried Gessulat
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Cited by
Year
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
20632014
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
6492019
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
2212017
ProteomicsDB
T Schmidt, P Samaras, M Frejno, S Gessulat, M Barnert, H Kienegger, ...
Nucleic acids research 46 (D1), D1271-D1281, 2018
2172018
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
2042020
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
1852020
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
1212021
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
482019
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
402021
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
342021
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
172023
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
92023
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
52021
A review of real-time models for transportation mode detection
S Gessulat
Free University of Berlin, Tech. Rep, 2013
32013
A deep learning model for the proteome-wide prediction of peptide tandem mass spectra
S Gessulat
Technische Universität München, 2020
12020
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, ...
An end-to-end machine learning workflow for MS-based proteomics
S Gessulat, T Schmidt, M Graber, SB Fredj, L Mamisashvili, P Samaras, ...
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