DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput von Demichev, Vadim, Messner, Christoph B., Vernardis, Spyros I., Lilley, Kathryn S., Ralser, Markus

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Time-resolved in vivo ubiquitinome profiling by DIA-MS reveals USP7 targets on a proteome-wide scale von Steger, Martin, Demichev, Vadim, Backman, Mattias, Ohmayer, Uli, Ihmor, Phillip, Müller, Stefan, Ralser, Markus, Daub, Henrik

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Increasing the throughput of sensitive proteomics by plexDIA von Derks, Jason, Leduc, Andrew, Wallmann, Georg, Huffman, R. Gray, Willetts, Matthew, Khan, Saad, Specht, Harrison, Ralser, Markus, Demichev, Vadim, Slavov, Nikolai

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dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts von Demichev, Vadim, Szyrwiel, Lukasz, Yu, Fengchao, Teo, Guo Ci, Rosenberger, George, Niewienda, Agathe, Ludwig, Daniela, Decker, Jens, Kaspar-Schoenefeld, Stephanie, Lilley, Kathryn S., Mülleder, Michael, Nesvizhskii, Alexey I., Ralser, Markus

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MSBooster: improving peptide identification rates using deep learning-based features von Yang, Kevin L., Yu, Fengchao, Teo, Guo Ci, Li, Kai, Demichev, Vadim, Ralser, Markus, Nesvizhskii, Alexey I.

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Ultra-fast proteomics with Scanning SWATH von Messner, Christoph B., Demichev, Vadim, Bloomfield, Nic, Yu, Jason S. L., White, Matthew, Kreidl, Marco, Egger, Anna-Sophia, Freiwald, Anja, Ivosev, Gordana, Wasim, Fras, Zelezniak, Aleksej, Jürgens, Linda, Suttorp, Norbert, Sander, Leif Erik, Kurth, Florian, Lilley, Kathryn S., Mülleder, Michael, Tate, Stephen, Ralser, Markus

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Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform von Yu, Fengchao, Teo, Guo Ci, Kong, Andy T., Fröhlich, Klemens, Li, Ginny Xiaohe, Demichev, Vadim, Nesvizhskii, Alexey I.

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High-throughput proteomics of nanogram-scale samples with Zeno SWATH MS von Wang, Ziyue, Mülleder, Michael, Batruch, Ihor, Chelur, Anjali, Textoris-Taube, Kathrin, Schwecke, Torsten, Hartl, Johannes, Causon, Jason, Castro-Perez, Jose, Demichev, Vadim, Tate, Stephen, Ralser, Markus

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Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments von Gatto, Laurent, Aebersold, Ruedi, Cox, Juergen, Demichev, Vadim, Derks, Jason, Emmott, Edward, Franks, Alexander M., Ivanov, Alexander R., Kelly, Ryan T., Khoury, Luke, Leduc, Andrew, MacCoss, Michael J., Nemes, Peter, Perlman, David H., Petelski, Aleksandra A., Rose, Christopher M., Schoof, Erwin M., Van Eyk, Jennifer, Vanderaa, Christophe, Yates, John R., Slavov, Nikolai

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Microbe capture by splenic macrophages triggers sepsis via T cell-death-dependent neutrophil lifespan shortening von Ioannou, Marianna, Hoving, Dennis, Aramburu, Iker Valle, Temkin, Mia I., De Vasconcelos, Nathalia M., Tsourouktsoglou, Theodora-Dorita, Wang, Qian, Boeing, Stefan, Goldstone, Robert, Vernardis, Spyros, Demichev, Vadim, Ralser, Markus, David, Sascha, Stahl, Klaus, Bode, Christian, Papayannopoulos, Venizelos

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CysQuant: Simultaneous quantification of cysteine oxidation and protein abundance using data dependent or independent acquisition mass spectrometry von Huang, Jingjing, Staes, An, Impens, Francis, Demichev, Vadim, Van Breusegem, Frank, Gevaert, Kris, Willems, Patrick

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Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance von Yu, Jason S. L., Correia-Melo, Clara, Zorrilla, Francisco, Herrera-Dominguez, Lucia, Wu, Mary Y., Hartl, Johannes, Campbell, Kate, Blasche, Sonja, Kreidl, Marco, Egger, Anna-Sophia, Messner, Christoph B., Demichev, Vadim, Freiwald, Anja, Mülleder, Michael, Howell, Michael, Berman, Judith, Patil, Kiran R., Alam, Mohammad Tauqeer, Ralser, Markus

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Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts von Zelezniak, Aleksej, Vowinckel, Jakob, Capuano, Floriana, Messner, Christoph B., Demichev, Vadim, Polowsky, Nicole, Mülleder, Michael, Kamrad, Stephan, Klaus, Bernd, Keller, Markus A., Ralser, Markus

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Inorganic sulfur fixation via a new homocysteine synthase allows yeast cells to cooperatively compensate for methionine auxotrophy von Yu, Jason S L, Heineike, Benjamin M, Hartl, Johannes, Aulakh, Simran K, Correia-Melo, Clara, Lehmann, Andrea, Lemke, Oliver, Agostini, Federica, Lee, Cory T, Demichev, Vadim, Messner, Christoph B, Mülleder, Michael, Ralser, Markus

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A serum proteome signature to predict mortality in severe COVID-19 patients von Völlmy, Franziska, van den Toorn, Henk, Zenezini Chiozzi, Riccardo, Zucchetti, Ottavio, Papi, Alberto, Volta, Carlo Alberto, Marracino, Luisa, Vieceli Dalla Sega, Francesco, Fortini, Francesca, Demichev, Vadim, Tober-Lau, Pinkus, Campo, Gianluca, Contoli, Marco, Ralser, Markus, Kurth, Florian, Spadaro, Savino, Rizzo, Paola, Heck, Albert Jr

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A multiplex protein panel assay for severity prediction and outcome prognosis in patients with COVID-19: An observational multi-cohort study von Wang, Ziyue, Cryar, Adam, Lemke, Oliver, Tober-Lau, Pinkus, Ludwig, Daniela, Helbig, Elisa Theresa, Hippenstiel, Stefan, Sander, Leif-Erik, Blake, Daniel, Lane, Catherine S., Sayers, Rebekah L., Mueller, Christoph, Zeiser, Johannes, Townsend, StJohn, Demichev, Vadim, Mülleder, Michael, Kurth, Florian, Sirka, Ernestas, Hartl, Johannes, Ralser, Markus

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Cystatin C is associated with adverse COVID-19 outcomes in diverse populations von Kleeman, Sam O., Cordioli, Mattia, Timmers, Paul R.H. J., Khan, Atlas, Tober-Lau, Pinkus, Kurth, Florian, Demichev, Vadim, Meyer, Hannah V., Wilson, James F., Ralser, Markus, Kiryluk, Krzysztof, Ganna, Andrea, Baillie, Kenneth, Janowitz, Tobias

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Ubiquitinomics: History, methods, and applications in basic research and drug discovery von Steger, Martin, Karayel, Özge, Demichev, Vadim

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STATISTICAL ANALYSIS OF AGRICULTURAL EFFICIENCY USING MACHINE LEARNING METHODS von Demichev, Vadim Vladimirovich

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Mass spectrometry‐based high‐throughput proteomics and its role in biomedical studies and systems biology von Messner, Christoph B., Demichev, Vadim, Wang, Ziyue, Hartl, Johannes, Kustatscher, Georg, Mülleder, Michael, Ralser, Markus

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