Vadim Demichev

7.7k total citations · 5 hit papers
36 papers, 2.8k citations indexed

About

Vadim Demichev is a scholar working on Molecular Biology, Spectroscopy and Infectious Diseases. According to data from OpenAlex, Vadim Demichev has authored 36 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Molecular Biology, 23 papers in Spectroscopy and 4 papers in Infectious Diseases. Recurrent topics in Vadim Demichev's work include Advanced Proteomics Techniques and Applications (23 papers), Mass Spectrometry Techniques and Applications (13 papers) and Metabolomics and Mass Spectrometry Studies (8 papers). Vadim Demichev is often cited by papers focused on Advanced Proteomics Techniques and Applications (23 papers), Mass Spectrometry Techniques and Applications (13 papers) and Metabolomics and Mass Spectrometry Studies (8 papers). Vadim Demichev collaborates with scholars based in Germany, United Kingdom and United States. Vadim Demichev's co-authors include Markus Ralser, Christoph B. Messner, Kathryn S. Lilley, Spyros I. Vernardis, Michael Mülleder, Guo Ci Teo, Fengchao Yu, Alexey I. Nesvizhskii, Łukasz Szyrwiel and Ginny Xiaohe Li and has published in prestigious journals such as Cell, Nature Communications and Nature Biotechnology.

In The Last Decade

Vadim Demichev

33 papers receiving 2.8k citations

Hit Papers

DIA-NN: neural networks a... 2019 2026 2021 2023 2019 2022 2021 2022 2023 400 800 1.2k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput
    2019 1.4k citations Vadim Demichev, Christoph B. Messner et al. Nature Methods profile →
  2. dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts
    2022 202 citations Vadim Demichev, Łukasz Szyrwiel et al. Nature Communications profile →
  3. Ultra-fast proteomics with Scanning SWATH
    2021 179 citations Christoph B. Messner, Vadim Demichev et al. Nature Biotechnology profile →
  4. Increasing the throughput of sensitive proteomics by plexDIA
    2022 138 citations Jason Derks, Andrew Leduc et al. Nature Biotechnology profile →
  5. Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform
    2023 119 citations Fengchao Yu, Guo Ci Teo et al. Nature Communications profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Vadim Demichev Germany 19 2.0k 1.1k 219 172 167 36 2.8k
Dorte B. Bekker‐Jensen Denmark 20 2.3k 1.2× 1.2k 1.0× 360 1.6× 196 1.1× 139 0.8× 24 3.0k
George Rosenberger Switzerland 27 2.8k 1.4× 2.1k 1.8× 202 0.9× 183 1.1× 160 1.0× 36 3.8k
Feng Yang United States 25 2.0k 1.0× 796 0.7× 140 0.6× 104 0.6× 230 1.4× 87 2.7k
Mathias Wilhelm Germany 29 2.5k 1.3× 1.4k 1.3× 452 2.1× 229 1.3× 248 1.5× 93 3.7k
Zhi Sun United States 24 3.0k 1.5× 1.8k 1.6× 271 1.2× 226 1.3× 230 1.4× 52 4.1k
Brian C. Searle United States 20 2.2k 1.1× 1.7k 1.4× 139 0.6× 83 0.5× 130 0.8× 59 2.9k
Subhakar Dey United States 9 2.7k 1.4× 2.1k 1.8× 206 0.9× 186 1.1× 142 0.9× 15 3.8k
Pedro Navarro Spain 17 3.0k 1.5× 2.2k 1.9× 204 0.9× 227 1.3× 258 1.5× 26 4.3k
Bettina Sarg Austria 32 2.0k 1.0× 566 0.5× 224 1.0× 124 0.7× 191 1.1× 105 3.1k
Ben C. Collins Switzerland 28 3.0k 1.5× 2.2k 1.9× 247 1.1× 235 1.4× 223 1.3× 54 4.3k

Countries citing papers authored by Vadim Demichev

Since Specialization
Citations

This map shows the geographic impact of Vadim Demichev's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Vadim Demichev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vadim Demichev more than expected).

Fields of papers citing papers by Vadim Demichev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Vadim Demichev. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Vadim Demichev. The network helps show where Vadim Demichev may publish in the future.

Co-authorship network of co-authors of Vadim Demichev

This figure shows the co-authorship network connecting the top 25 collaborators of Vadim Demichev. A scholar is included among the top collaborators of Vadim Demichev based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Vadim Demichev. Vadim Demichev is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Willems, Patrick J., et al.. (2025). Data-Independent Immunopeptidomics Discovery of Low-Abundant Bacterial Epitopes. Journal of Proteome Research. 24(12). 6295–6304. 1 indexed citations
2.
Sinn, Ludwig, Ihor Batruch, Patrick Pribil, et al.. (2025). Performance Characteristics of Zeno Trap Scanning DIA for Sensitive and Quantitative Proteomics at High Throughput. PROTEOMICS. 26(1). 68–81.
3.
Pfeuffer, Julianus, Hong Wang, Ping Zheng, et al.. (2024). quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data. Nature Methods. 21(9). 1603–1607. 26 indexed citations
4.
Szyrwiel, Łukasz, Christoph Gille, Michael Mülleder, Vadim Demichev, & Markus Ralser. (2023). Fast proteomics with dia‐PASEF and analytical flow‐rate chromatography. PROTEOMICS. 24(1-2). e2300100–e2300100. 19 indexed citations
5.
White, Matthew, Ludwig Sinn, D. Marc Jones, et al.. (2023). Oxonium ion scanning mass spectrometry for large-scale plasma glycoproteomics. Nature Biomedical Engineering. 8(3). 233–247. 16 indexed citations
6.
Talwar, Deepti, Colin G. Miller, Łukasz Szyrwiel, et al.. (2023). The GAPDH redox switch safeguards reductive capacity and enables survival of stressed tumour cells. Nature Metabolism. 5(4). 660–676. 59 indexed citations
7.
Kamrad, Stephan, Clara Correia‐Melo, Łukasz Szyrwiel, et al.. (2023). Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC. Nature Microbiology. 8(3). 441–454. 14 indexed citations
8.
Huang, Jingjing, An Staes, Francis Impens, et al.. (2023). CysQuant: Simultaneous quantification of cysteine oxidation and protein abundance using data dependent or independent acquisition mass spectrometry. Redox Biology. 67. 102908–102908. 14 indexed citations
9.
Correia‐Melo, Clara, Stephan Kamrad, Roland Tengölics, et al.. (2023). Cell-cell metabolite exchange creates a pro-survival metabolic environment that extends lifespan. Cell. 186(1). 63–79.e21. 32 indexed citations
10.
Yu, Fengchao, Guo Ci Teo, Andy T. Kong, et al.. (2023). Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform. Nature Communications. 14(1). 4154–4154. 119 indexed citations breakdown →
11.
Messner, Christoph B., Vadim Demichev, Johannes Hartl, et al.. (2022). Mass spectrometry‐based high‐throughput proteomics and its role in biomedical studies and systems biology. PROTEOMICS. 23(7-8). e2200013–e2200013. 55 indexed citations
12.
Demichev, Vadim, Łukasz Szyrwiel, Fengchao Yu, et al.. (2022). dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts. Nature Communications. 13(1). 3944–3944. 202 indexed citations breakdown →
13.
Ιωάννου, Μαριάννα, Dennis Hoving, Iker Valle Aramburu, et al.. (2022). Microbe capture by splenic macrophages triggers sepsis via T cell-death-dependent neutrophil lifespan shortening. Nature Communications. 13(1). 4658–4658. 27 indexed citations
14.
Mülleder, Michael, Ihor Batruch, Kathrin Textoris‐Taube, et al.. (2022). High-throughput proteomics of nanogram-scale samples with Zeno SWATH MS. eLife. 11. 42 indexed citations
15.
Yu, Jason, Benjamin M. Heineike, Johannes Hartl, et al.. (2022). Inorganic sulfur fixation via a new homocysteine synthase allows yeast cells to cooperatively compensate for methionine auxotrophy. PLoS Biology. 20(12). e3001912–e3001912. 6 indexed citations
16.
Derks, Jason, Andrew Leduc, Georg Wallmann, et al.. (2022). Increasing the throughput of sensitive proteomics by plexDIA. Nature Biotechnology. 41(1). 50–59. 138 indexed citations breakdown →
17.
Messner, Christoph B., Vadim Demichev, Nic Bloomfield, et al.. (2021). Ultra-fast proteomics with Scanning SWATH. Nature Biotechnology. 39(7). 846–854. 179 indexed citations breakdown →
18.
Toorn, Henk van den, Riccardo Zenezini Chiozzi, Ottavio Zucchetti, et al.. (2021). A serum proteome signature to predict mortality in severe COVID-19 patients. Life Science Alliance. 4(9). e202101099–e202101099. 58 indexed citations
19.
Demichev, Vadim, Christoph B. Messner, Spyros I. Vernardis, Kathryn S. Lilley, & Markus Ralser. (2019). DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nature Methods. 17(1). 41–44. 1410 indexed citations breakdown →
20.
Demichev, Vadim. (2014). Functional Central Limit Theorem for Excursion set Volumes of Quasi-Associated Random Fields. Journal of Mathematical Sciences. 204(1). 69–77.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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