Dániel Süveges

8.4k total citations
18 papers, 590 citations indexed

About

Dániel Süveges is a scholar working on Molecular Biology, Genetics and Cell Biology. According to data from OpenAlex, Dániel Süveges has authored 18 papers receiving a total of 590 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 9 papers in Genetics and 3 papers in Cell Biology. Recurrent topics in Dániel Süveges's work include Genetic Associations and Epidemiology (7 papers), Protein Structure and Dynamics (5 papers) and Bioinformatics and Genomic Networks (4 papers). Dániel Süveges is often cited by papers focused on Genetic Associations and Epidemiology (7 papers), Protein Structure and Dynamics (5 papers) and Bioinformatics and Genomic Networks (4 papers). Dániel Süveges collaborates with scholars based in United Kingdom, Greece and Hungary. Dániel Süveges's co-authors include László Nyitray, Zoltán Gáspári, Gábor Tóth, Eleftheria Zeggini, Arthur Gilly, George Dedoussis, Lorraine Southam, Emmanouil Tsafantakis, Nigel W. Rayner and Jeremy Schwartzentruber and has published in prestigious journals such as Journal of Biological Chemistry, Nature Communications and Nature Genetics.

In The Last Decade

Dániel Süveges

18 papers receiving 588 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dániel Süveges United Kingdom 12 345 202 69 64 51 18 590
Hyun‐Jun Nam South Korea 9 501 1.5× 223 1.1× 55 0.8× 79 1.2× 49 1.0× 15 763
Zhaolong Li China 8 362 1.0× 151 0.7× 32 0.5× 40 0.6× 40 0.8× 19 511
Jerome Lin United States 7 274 0.8× 150 0.7× 33 0.5× 68 1.1× 26 0.5× 10 518
Jill Holbrook Germany 8 727 2.1× 170 0.8× 42 0.6× 34 0.5× 21 0.4× 10 891
Jin Inoue Japan 12 459 1.3× 144 0.7× 23 0.3× 22 0.3× 48 0.9× 25 621
Johannes Koch Austria 17 688 2.0× 72 0.4× 25 0.4× 55 0.9× 39 0.8× 27 933
Greg Slodkowicz United Kingdom 8 553 1.6× 125 0.6× 37 0.5× 25 0.4× 58 1.1× 10 740
Reidun Kopperud Norway 11 427 1.2× 59 0.3× 54 0.8× 47 0.7× 24 0.5× 16 635
Tine Skovgaard Denmark 8 483 1.4× 49 0.2× 64 0.9× 87 1.4× 31 0.6× 12 650
Jiuya He United Kingdom 20 1.7k 4.8× 154 0.8× 96 1.4× 99 1.5× 79 1.5× 26 1.8k

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-authorship network of co-authors of Dániel Süveges

This figure shows the co-authorship network connecting the top 25 collaborators of Dániel Süveges. A scholar is included among the top collaborators of Dániel Süveges 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 Dániel Süveges. Dániel Süveges is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Tirunagari, Santosh, Shyamasree Saha, Aravind Venkatesan, et al.. (2025). Lit-OTAR framework for extracting biological evidences from literature. Bioinformatics. 41(4). 4 indexed citations
2.
Barrio‐Hernandez, Inigo, Jeremy Schwartzentruber, Anjali Shrivastava, et al.. (2023). Network expansion of genetic associations defines a pleiotropy map of human cell biology. Nature Genetics. 55(3). 389–398. 42 indexed citations
3.
Kuchenbaecker, Karoline, Arthur Gilly, Dániel Süveges, et al.. (2022). Insights into the genetic architecture of haematological traits from deep phenotyping and whole-genome sequencing for two Mediterranean isolated populations. Scientific Reports. 12(1). 1131–1131. 2 indexed citations
4.
Finan, Chris, Dániel Süveges, Tesfaye Sisay Tessema, et al.. (2021). Replication of HLA class II locus association with susceptibility to podoconiosis in three Ethiopian ethnic groups. Scientific Reports. 11(1). 3285–3285. 4 indexed citations
5.
Gilly, Arthur, Andrei Barysenka, Iris Fischer, et al.. (2020). Whole-genome sequencing analysis of the cardiometabolic proteome. Nature Communications. 11(1). 6336–6336. 61 indexed citations
6.
Sollis, Elliot, Annalisa Buniello, María Cerezo, et al.. (2019). User-focused development of the NHGRI-EBI Genome-Wide Association Studies Catalog. Faculty of 1000 Research Ltd. 8. 1 indexed citations
7.
Süveges, Dániel, Klaudia Walter, Kousik Kundu, et al.. (2019). Population‐wide copy number variation calling using variant call format files from 6,898 individuals. Genetic Epidemiology. 44(1). 79–89. 3 indexed citations
8.
Gilly, Arthur, Lorraine Southam, Dániel Süveges, et al.. (2018). Very low-depth whole-genome sequencing in complex trait association studies. Bioinformatics. 35(15). 2555–2561. 60 indexed citations
9.
Grarup, Niels, Ida Moltke, Mette K. Andersen, et al.. (2018). Loss-of-function variants in ADCY3 increase risk of obesity and type 2 diabetes. Nature Genetics. 50(2). 172–174. 119 indexed citations
10.
Casalone, Elisabetta, Ioanna Tachmazidou, Eleni Zengini, et al.. (2018). A novel variant in GLIS3 is associated with osteoarthritis. Annals of the Rheumatic Diseases. 77(4). 620–623. 29 indexed citations
11.
Southam, Lorraine, Arthur Gilly, Dániel Süveges, et al.. (2017). Whole genome sequencing and imputation in isolated populations identify genetic associations with medically-relevant complex traits. Nature Communications. 8(1). 15606–15606. 52 indexed citations
12.
Gáspári, Zoltán, Dániel Süveges, András Perczel, László Nyitray, & Gábor Tóth. (2012). Charged single alpha-helices in proteomes revealed by a consensus prediction approach. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 1824(4). 637–646. 27 indexed citations
13.
Rapali, Péter, László Radnai, Dániel Süveges, et al.. (2011). Directed Evolution Reveals the Binding Motif Preference of the LC8/DYNLL Hub Protein and Predicts Large Numbers of Novel Binders in the Human Proteome. PLoS ONE. 6(4). e18818–e18818. 55 indexed citations
14.
Radnai, László, Péter Rapali, Dániel Süveges, et al.. (2010). Affinity, Avidity, and Kinetics of Target Sequence Binding to LC8 Dynein Light Chain Isoforms. Journal of Biological Chemistry. 285(49). 38649–38657. 32 indexed citations
15.
Szappanos, Balázs, Dániel Süveges, László Nyitray, András Perczel, & Zoltán Gáspári. (2010). Folded‐unfolded cross‐predictions and protein evolution: The case study of coiled‐coils. FEBS Letters. 584(8). 1623–1627. 23 indexed citations
16.
Espinoza‐Fonseca, L. Michel, Dániel Süveges, Zoltán Gáspári, Gábor Tóth, & László Nyitray. (2009). Role of Cationic Residues in Fine Tuning the Flexibility of Charged Single α-helices. Biophysical Journal. 96(3). 322a–322a. 2 indexed citations
17.
Süveges, Dániel, Zoltán Gáspári, Gábor Tóth, & László Nyitray. (2008). Charged single α‐helix: A versatile protein structural motif. Proteins Structure Function and Bioinformatics. 74(4). 905–916. 60 indexed citations
18.
Brown, Jerry H., Yuting Yang, Samudrala Gourinath, et al.. (2007). An Unstable Head–Rod Junction May Promote Folding into the Compact Off-State Conformation of Regulated Myosins. Journal of Molecular Biology. 375(5). 1434–1443. 14 indexed citations

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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