Dániel Süveges

8.4k citations
18 papers · 590 indexed · h-index 12

Impact in

  • Genetics top 10%
    • Genetic Associations and Epidemiology
    • Genetic and phenotypic traits in livestock
    • Genetic Mapping and Diversity in Plants and Animals
    • Protein Structure and Dynamics
    • RNA and protein synthesis mechanisms
    • Bioinformatics and Genomic Networks
    • RNA Research and Splicing

Papers in

    • Genetic Associations and Epidemiology 7
    • Genetic and phenotypic traits in livestock 2
    • Cancer-related molecular mechanisms research 2

Dániel Süveges

18 papers receiving 588 citations

Peers

Dániel Süveges
Comparison fields: 5 of 85
  • Genetics 202
  • Molecular Biology 345
  • Cell Biology 69
  • Cancer Research 51
  • Endocrine and Autonomic Systems 22
Replace Jerome Lin with:
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Benoit Devogelaere Belgium
Tiffany Amariuta United States
Dániel Süveges relative to Jerome Lin United States Jerome Lin's profile →
Citations per field
00.5×
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Citations per year

Countries citing papers authored by Dániel Süveges

Since Specialization
Citations

This map shows the geographic impact of Dániel Süveges'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 Dániel Süveges with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dániel Süveges more than expected).

Fields of papers citing papers by Dániel Süveges

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dániel Süveges. 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 Dániel Süveges. The network helps show where Dániel Süveges may publish in the future.

Co-authors

The 25 scholars most cited alongside Dániel Süveges, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dániel Süveges Line = papers co-authored together Dániel Süveges links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 2018119
2 202061
3 201860
4 200860
5 201155
6 201752
7 202342
8 201032
9 201829
10 201227
11 201023
12 200714
13 20254
14 20214
15 20193
16 20092
17 20222
18 20191

About Dániel Süveges

Dániel Süveges is a scholar working on Genetics, Cancer Research, Cell Biology, Molecular Biology and Spectroscopy, having authored 18 papers that have together received 590 indexed citations. Recurring topics across this work include Genetic Associations and Epidemiology (7 papers), Protein Structure and Dynamics (5 papers), RNA and protein synthesis mechanisms (4 papers), Bioinformatics and Genomic Networks (4 papers), Genetic and phenotypic traits in livestock (2 papers), Cancer-related molecular mechanisms research (2 papers), Microtubule and mitosis dynamics (2 papers) and Advanced Proteomics Techniques and Applications (2 papers). The work is most often cited by research in Genetics (202 citations), Molecular Biology (345 citations), Cell Biology (69 citations), Cancer Research (51 citations) and Endocrine and Autonomic Systems (22 citations). Dániel Süveges has collaborated with scholars based in United Kingdom, Greece and Hungary. Frequent co-authors include László Nyitray, Zoltán Gáspári, Eleftheria Zeggini, Gábor Tóth, Arthur Gilly, George Dedoussis, Lorraine Southam, Emmanouil Tsafantakis, Nigel W. Rayner and Jeremy Schwartzentruber. Their work appears in journals such as Nature Genetics, Scientific Reports, Nature Communications, Bioinformatics and Biochimica et Biophysica Acta (BBA) - Proteins 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.

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