DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput V Demichev, CB Messner, SI Vernardis, KS Lilley, M Ralser Nature methods 17 (1), 41-44, 2020 | 1131 | 2020 |
Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection CB Messner, V Demichev, D Wendisch, L Michalick, M White, A Freiwald, ... Cell systems 11 (1), 11-24. e4, 2020 | 480 | 2020 |
Ultra-fast proteomics with Scanning SWATH CB Messner, V Demichev, N Bloomfield, JSL Yu, M White, M Kreidl, ... Nature biotechnology 39 (7), 846-854, 2021 | 221 | 2021 |
Complement activation induces excessive T cell cytotoxicity in severe COVID-19 P Georg, R Astaburuaga-García, L Bonaguro, S Brumhard, L Michalick, ... Cell 185 (3), 493-512. e25, 2022 | 155 | 2022 |
dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts V Demichev, L Szyrwiel, F Yu, GC Teo, G Rosenberger, A Niewienda, ... Nature communications 13 (1), 3944, 2022 | 154 | 2022 |
A time-resolved proteomic and prognostic map of COVID-19 V Demichev, P Tober-Lau, O Lemke, T Nazarenko, C Thibeault, ... Cell systems 12 (8), 780-794. e7, 2021 | 145 | 2021 |
Increasing the throughput of sensitive proteomics by plexDIA J Derks, A Leduc, G Wallmann, RG Huffman, M Willetts, S Khan, H Specht, ... Nature biotechnology 41 (1), 50-59, 2023 | 131 | 2023 |
Machine learning predicts the yeast metabolome from the quantitative proteome of kinase knockouts A Zelezniak, J Vowinckel, F Capuano, CB Messner, V Demichev, ... Cell systems 7 (3), 269-283. e6, 2018 | 94 | 2018 |
Time-resolved in vivo ubiquitinome profiling by DIA-MS reveals USP7 targets on a proteome-wide scale M Steger, V Demichev, M Backman, U Ohmayer, P Ihmor, S Müller, ... Nature communications 12 (1), 5399, 2021 | 78 | 2021 |
Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments L Gatto, R Aebersold, J Cox, V Demichev, J Derks, E Emmott, AM Franks, ... Nature methods 20 (3), 375-386, 2023 | 72 | 2023 |
Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance JSL Yu, C Correia-Melo, F Zorrilla, L Herrera-Dominguez, MY Wu, J Hartl, ... Nature microbiology 7 (4), 542-555, 2022 | 68 | 2022 |
A serum proteome signature to predict mortality in severe COVID-19 patients F Völlmy, H Van Den Toorn, RZ Chiozzi, O Zucchetti, A Papi, CA Volta, ... Life Science Alliance 4 (9), 2021 | 67 | 2021 |
MSBooster: improving peptide identification rates using deep learning-based features KL Yang, F Yu, GC Teo, K Li, V Demichev, M Ralser, AI Nesvizhskii Nature Communications 14 (1), 4539, 2023 | 48 | 2023 |
A proteomic survival predictor for COVID-19 patients in intensive care V Demichev, P Tober-Lau, T Nazarenko, O Lemke, S Kaur Aulakh, ... PLOS Digital Health 1 (1), e0000007, 2022 | 43 | 2022 |
Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform F Yu, GC Teo, AT Kong, K Fröhlich, GX Li, V Demichev, AI Nesvizhskii Nature Communications 14 (1), 4154, 2023 | 40 | 2023 |
Mass spectrometry‐based high‐throughput proteomics and its role in biomedical studies and systems biology CB Messner, V Demichev, Z Wang, J Hartl, G Kustatscher, M Mülleder, ... Proteomics 23 (7-8), 2200013, 2023 | 39 | 2023 |
High sensitivity dia-PASEF proteomics with DIA-NN and FragPipe V Demichev, F Yu, GC Teo, L Szyrwiel, GA Rosenberger, J Decker, ... Biorxiv, 2021.03. 08.434385, 2021 | 35 | 2021 |
The proteomic landscape of genome-wide genetic perturbations CB Messner, V Demichev, J Muenzner, SK Aulakh, N Barthel, A Röhl, ... Cell 186 (9), 2018-2034. e21, 2023 | 31 | 2023 |
High-throughput proteomics of nanogram-scale samples with Zeno SWATH MS Z Wang, M Mülleder, I Batruch, A Chelur, K Textoris-Taube, T Schwecke, ... Elife 11, e83947, 2022 | 31 | 2022 |
Cell-cell metabolite exchange creates a pro-survival metabolic environment that extends lifespan C Correia-Melo, S Kamrad, R Tengölics, CB Messner, P Trebulle, ... Cell 186 (1), 63-79. e21, 2023 | 27 | 2023 |