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David Teschner
David Teschner
Institut für Informatik JGU Mainz
uni-mainz.de의 이메일 확인됨
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midiaPASEF maximizes information content in data-independent acquisition proteomics
U Distler, MK Łącki, MP Startek, D Teschner, S Brehmer, J Decker, ...
BioRxiv, 2023.01. 30.526204, 2023
302023
Ionmob: a Python package for prediction of peptide collisional cross-section values
D Teschner, D Gomez-Zepeda, A Declercq, MK Łącki, S Avci, K Bob, ...
Bioinformatics 39 (9), btad486, 2023
132023
midiaPASEF maximizes information content in data-independent acquisition proteomics. bioRxiv 2023, 2023.2001. 2030.526204
U Distler, MK Łącki, MP Startek, D Teschner, S Brehmer, J Decker, ...
DOI 10 (2023.01), 30.526204, 0
5
Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data
K Bob, D Teschner, T Kemmer, D Gomez-Zepeda, S Tenzer, B Schmidt, ...
BMC bioinformatics 23 (1), 287, 2022
42022
CorCast: A Distributed Architecture for Bayesian Epidemic Nowcasting and its Application to District-Level SARS-CoV-2 Infection Numbers in Germany
AK Hildebrandt, K Bob, D Teschner, T Kemmer, J Leclaire, B Schmidt, ...
medRxiv, 2021.06. 02.21258209, 2021
42021
Locality-sensitive hashing enables signal classification in high-throughput mass spectrometry raw data at scale
K Bob, D Teschner, T Kemmer, D Gomez-Zepeda, S Tenzer, B Schmidt, ...
bioRxiv, 2021.07. 01.450702, 2021
2021
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