Milan Simonovic
- Cancer Research top 0.1%
- Molecular Biology top 0.05%
- Bioinformatics and Genomic Networks 9
- Biomedical Text Mining and Ontologies 4
- Metabolomics and Mass Spectrometry Studies 2
- Genomics and Phylogenetic Studies 2
- Microbial Metabolic Engineering and Bioproduction 2
- Machine Learning in Bioinformatics 1
- Ubiquitin and proteasome pathways 1
- Immunology top 0.5%
- Aging top 1%
- Biological Psychiatry top 1%
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- Advanced Proteomics Techniques and Applications 5
Milan Simonovic
10 papers receiving 33.5k citations
Hit Papers
Peers
Comparison fields: 5 of 194
- Cancer Research 5.2k
- Molecular Biology 21.7k
- Immunology 3.5k
- Aging 251
- Biological Psychiatry 351
Countries citing papers authored by Milan Simonovic
This map shows the geographic impact of Milan Simonovic'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 Milan Simonovic with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Milan Simonovic more than expected).
Fields of papers citing papers by Milan Simonovic
This network shows the impact of papers produced by Milan Simonovic. 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 Milan Simonovic. The network helps show where Milan Simonovic may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Milan Simonovic, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 52 | |
| 2 | STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasetsbreakdown → | 2018 | 11663 |
| 3 | The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessiblebreakdown → | 2016 | 5222 |
| 4 | Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell‐linesbreakdown → | 2015 | 384 |
| 5 | STRING v10: protein–protein interaction networks, integrated over the tree of lifebreakdown → | 2014 | 7783 |
| 6 | Drosophila H1 Regulates the Genetic Activity of Heterochromatin by Recruitment of Su(var)3-9 | 2013 | 3 |
| 7 | 2012 | 362 | |
| 8 | STRING v9.1: protein-protein interaction networks, with increased coverage and integrationbreakdown → | 2012 | 3505 |
| 9 | The STRING database in 2011: functional interaction networks of proteins, globally integrated and scoredbreakdown → | 2010 | 2796 |
| 10 | STRING 8--a global view on proteins and their functional interactions in 630 organismsbreakdown → | 2008 | 1950 |
About Milan Simonovic
Milan Simonovic is a scholar working on Spectroscopy, Molecular Biology and Infectious Diseases, having authored 10 papers that have together received 33.7k indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (9 papers), Advanced Proteomics Techniques and Applications (5 papers), Biomedical Text Mining and Ontologies (4 papers), Metabolomics and Mass Spectrometry Studies (2 papers), Genomics and Phylogenetic Studies (2 papers), Microbial Metabolic Engineering and Bioproduction (2 papers), Machine Learning in Bioinformatics (1 paper) and Ubiquitin and proteasome pathways (1 paper). The work is most often cited by research in Cancer Research (5.2k citations), Molecular Biology (21.7k citations) and Immunology (3.5k citations). Milan Simonovic has collaborated with scholars based in Switzerland, Germany and Denmark. Frequent co-authors include Christian von Mering, Lars Juhl Jensen, Peer Bork, Damian Szklarczyk, Stefan Wyder, Alexander Röth, Michael Kuhn, Jaime Huerta‐Cepas, Nadezhda T. Doncheva and John H. Morris. Their work appears in journals such as Nucleic Acids Research, Molecular & Cellular Proteomics and PROTEOMICS.
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.