Tomislav Šmuc
Impact in
- Aging top 5%
- Molecular Biology top 2%
- Genomics and Phylogenetic Studies
- RNA Research and Splicing
- Bioinformatics and Genomic Networks
- RNA modifications and cancer
- Epigenetics and DNA Methylation
- RNA and protein synthesis mechanisms
Papers in
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- Genomics and Phylogenetic Studies 5
- Bioinformatics and Genomic Networks 5
- Gene expression and cancer classification 4
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- Data Mining Algorithms and Applications 9
- Co-authors
- Fran Supek (16 shared papers)Nives Škunca (3 shared papers)Matko Bošnjak (5 shared papers)Marijeta Kralj (6 shared papers)Anita Kriško (3 shared papers)Nino Antulov-Fantulin (5 shared papers)Vedrana Vidulin (3 shared papers)Sašo Džeroski (7 shared papers)
In The Last Decade
Tomislav Šmuc
66 papers receiving 5.4k citations
Tomislav Šmuc's Hit Papers
Peers
Comparison fields: 5 of 182
- Aging 79
- Molecular Biology 2.4k
- Plant Science 1.1k
- Cancer Research 332
- Genetics 627
Countries citing papers authored by Tomislav Šmuc
This map shows the geographic impact of Tomislav Šmuc'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 Tomislav Šmuc with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomislav Šmuc more than expected).
Fields of papers citing papers by Tomislav Šmuc
This network shows the impact of papers produced by Tomislav Šmuc. 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 Tomislav Šmuc. The network helps show where Tomislav Šmuc may publish in the future.
Co-authors
The 25 scholars most cited alongside Tomislav Šmuc, 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 70 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms Hit paper breakdown → | 2011 | 4436 |
| 2 | 2016 | 84 | |
| 3 | 2015 | 72 | |
| 4 | 2010 | 67 | |
| 5 | 2007 | 59 | |
| 6 | 2013 | 45 | |
| 7 | 2007 | 43 | |
| 8 | 1994 | 40 | |
| 9 | 2008 | 40 | |
| 10 | 2013 | 32 | |
| 11 | 2012 | 28 | |
| 12 | 2011 | 26 | |
| 13 | 2005 | 26 | |
| 14 | 2008 | 25 | |
| 15 | 2017 | 24 | |
| 16 | 2007 | 23 | |
| 17 | 2021 | 22 | |
| 18 | 2007 | 21 | |
| 19 | 2011 | 21 | |
| 20 | 2014 | 21 |
About Tomislav Šmuc
Tomislav Šmuc is a scholar working on Molecular Biology, Information Systems, Artificial Intelligence, Computational Theory and Mathematics and Cardiology and Cardiovascular Medicine, having authored 70 papers that have together received 5.4k indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (9 papers), Computational Drug Discovery Methods (6 papers), Genomics and Phylogenetic Studies (5 papers), Complex Systems and Time Series Analysis (5 papers), Bioinformatics and Genomic Networks (5 papers), Rough Sets and Fuzzy Logic (4 papers), Gene expression and cancer classification (4 papers) and Heart Rate Variability and Autonomic Control (4 papers). The work is most often cited by research in Aging (79 citations), Molecular Biology (2.4k citations), Plant Science (1.1k citations), Cancer Research (332 citations) and Genetics (627 citations). Tomislav Šmuc has collaborated with scholars based in Croatia, Slovenia and Spain. Frequent co-authors include Fran Supek, Nives Škunca, Matko Bošnjak, Marijeta Kralj, Anita Kriško, Nino Antulov-Fantulin, Vedrana Vidulin, Sašo Džeroski, Marko Marjanović and Mile Šikić. Their work appears in journals such as Molecules, PLoS ONE, Scientific Reports, Bioinformatics and Investigational New Drugs.
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.