Ivan Matić
Impact in
- Physiology top 0.5%
- Calcium signaling and nucleotide metabolism
- Oncology top 1%
- PARP inhibition in cancer therapy
- Peptidase Inhibition and Analysis
Papers in
-
- Ubiquitin and proteasome pathways 14
- RNA and protein synthesis mechanisms 8
- Glycosylation and Glycoproteins Research 7
- DNA Repair Mechanisms 6
- Genomics and Chromatin Dynamics 5
- Oncology 18
- PARP inhibition in cancer therapy 14
- Co-authors
- Matthias Mann (10 shared papers)Ronald T. Hay (9 shared papers)Michael H. Tatham (6 shared papers)Thomas Colby (16 shared papers)Jürgen Cox (2 shared papers)Ivan Ahel (11 shared papers)Juán José Bonfiglio (10 shared papers)Maximiliane Hilger (2 shared papers)
- Journals
- Molecular Cell (5 papers)Nature Communications (4 papers)Journal of Biological Chemistry (3 papers)Science Signaling (3 papers)Journal of Proteome Research (2 papers)
- Partner nations
- GermanyUnited KingdomNetherlands
In The Last Decade
Ivan Matić
44 papers receiving 4.4k citations
Ivan Matić's Hit Papers
Peers
Comparison fields: 5 of 110
- Physiology 340
- Oncology 1.8k
- Molecular Biology 3.4k
- Immunology 790
- Parasitology 166
Countries citing papers authored by Ivan Matić
This map shows the geographic impact of Ivan Matić'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 Ivan Matić with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivan Matić more than expected).
Fields of papers citing papers by Ivan Matić
This network shows the impact of papers produced by Ivan Matić. 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 Ivan Matić. The network helps show where Ivan Matić may publish in the future.
Co-authors
The 25 scholars most cited alongside Ivan Matić, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics Hit paper breakdown → | 2009 | 641 |
| 2 | 2009 | 413 | |
| 3 | 2010 | 284 | |
| 4 | 2017 | 272 | |
| 5 | 2010 | 260 | |
| 6 | 2007 | 241 | |
| 7 | 2016 | 222 | |
| 8 | 2018 | 192 | |
| 9 | 2016 | 190 | |
| 10 | 2014 | 166 | |
| 11 | 2011 | 139 | |
| 12 | 2011 | 139 | |
| 13 | 2008 | 134 | |
| 14 | 2010 | 133 | |
| 15 | 2015 | 115 | |
| 16 | 2012 | 97 | |
| 17 | 2018 | 85 | |
| 18 | 2017 | 85 | |
| 19 | 2015 | 76 | |
| 20 | 2020 | 72 |
About Ivan Matić
Ivan Matić is a scholar working on Molecular Biology, Oncology, Immunology, Spectroscopy and Epidemiology, having authored 45 papers that have together received 4.4k indexed citations. Recurring topics across this work include PARP inhibition in cancer therapy (14 papers), Ubiquitin and proteasome pathways (14 papers), Toxin Mechanisms and Immunotoxins (13 papers), RNA and protein synthesis mechanisms (8 papers), Advanced Proteomics Techniques and Applications (8 papers), Glycosylation and Glycoproteins Research (7 papers), DNA Repair Mechanisms (6 papers) and Genomics and Chromatin Dynamics (5 papers). The work is most often cited by research in Physiology (340 citations), Oncology (1.8k citations), Molecular Biology (3.4k citations), Immunology (790 citations) and Parasitology (166 citations). Ivan Matić has collaborated with scholars based in Germany, United Kingdom and Netherlands. Frequent co-authors include Matthias Mann, Ronald T. Hay, Michael H. Tatham, Thomas Colby, Jürgen Cox, Ivan Ahel, Juán José Bonfiglio, Maximiliane Hilger, Orsolya Leidecker and Matthias Selbach. Their work appears in journals such as Molecular Cell, Nature Communications, Journal of Biological Chemistry, Science Signaling and Journal of Proteome Research.
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.