Miklós Cserző

2.4k total citations · 1 hit paper
25 papers, 1.7k citations indexed

About

Miklós Cserző is a scholar working on Molecular Biology, Materials Chemistry and Cellular and Molecular Neuroscience. According to data from OpenAlex, Miklós Cserző has authored 25 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 5 papers in Materials Chemistry and 3 papers in Cellular and Molecular Neuroscience. Recurrent topics in Miklós Cserző's work include Protein Structure and Dynamics (8 papers), RNA and protein synthesis mechanisms (7 papers) and Machine Learning in Bioinformatics (6 papers). Miklós Cserző is often cited by papers focused on Protein Structure and Dynamics (8 papers), RNA and protein synthesis mechanisms (7 papers) and Machine Learning in Bioinformatics (6 papers). Miklós Cserző collaborates with scholars based in Hungary, Italy and United States. Miklós Cserző's co-authors include István Simon, Gunnar von Heijne, Arne Elofsson, Erik Jakob Wallin, Tamás Vicsek, Viktor Horváth, Birgit Eisenhaber, Frank Eisenhaber, László Hunyady and László Szidonya and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Miklós Cserző

25 papers receiving 1.6k citations

Hit Papers

Prediction of transmembrane alpha-helices in prokaryotic ... 1997 2026 2006 2016 1997 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Miklós Cserző Hungary 15 1.1k 225 197 135 128 25 1.7k
Christopher Hudson Moore United States 19 685 0.6× 119 0.5× 71 0.4× 275 2.0× 58 0.5× 50 1.5k
Wilfred H. Tang United States 12 1.5k 1.4× 110 0.5× 154 0.8× 110 0.8× 101 0.8× 16 2.3k
Tzviya Zeev‐Ben‐Mordehai United Kingdom 20 1.1k 1.0× 232 1.0× 128 0.6× 196 1.5× 87 0.7× 34 1.8k
Oleg A. Igoshin United States 31 1.5k 1.4× 838 3.7× 177 0.9× 82 0.6× 345 2.7× 95 2.3k
Ian Stansfield United Kingdom 24 2.3k 2.1× 220 1.0× 200 1.0× 63 0.5× 69 0.5× 49 2.6k
Léon Espinosa France 27 1.1k 1.0× 444 2.0× 280 1.4× 53 0.4× 545 4.3× 46 2.2k
Edouard Yeramian France 19 793 0.7× 199 0.9× 97 0.5× 63 0.5× 106 0.8× 38 1.4k
B. Edwin Blaisdell United States 22 1.2k 1.1× 215 1.0× 152 0.8× 59 0.4× 155 1.2× 37 1.7k
F. Jon Kull United States 27 2.0k 1.8× 248 1.1× 203 1.0× 159 1.2× 77 0.6× 74 3.1k
Anat Bren Israel 23 2.0k 1.8× 1.2k 5.1× 131 0.7× 105 0.8× 268 2.1× 31 2.5k

Countries citing papers authored by Miklós Cserző

Since Specialization
Citations

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

Fields of papers citing papers by Miklós Cserző

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Miklós Cserző

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

All Works

20 of 20 papers shown
1.
Tóth, Dániel J., et al.. (2025). Differential activation of the inositol 5-phosphatase SHIP2 by EGF and insulin signaling pathways. Journal of Biological Chemistry. 301(7). 110275–110275. 1 indexed citations
2.
Magyar, Csaba, et al.. (2023). Molecular Dynamics Simulation as a Tool to Identify Mutual Synergistic Folding Proteins. International Journal of Molecular Sciences. 24(2). 1790–1790. 2 indexed citations
3.
Turu, Gábor, András Dávid Tóth, Miklós Cserző, et al.. (2021). Biased Coupling to β-Arrestin of Two Common Variants of the CB2 Cannabinoid Receptor. Frontiers in Endocrinology. 12. 714561–714561. 14 indexed citations
4.
Magyar, Csaba, et al.. (2021). Origin of Increased Solvent Accessibility of Peptide Bonds in Mutual Synergetic Folding Proteins. International Journal of Molecular Sciences. 22(24). 13404–13404. 1 indexed citations
5.
Cserháti, Mátyás, et al.. (2011). Prediction of new abiotic stress genes in Arabidopsis thaliana and Oryza sativa according to enumeration-based statistical analysis. Molecular Genetics and Genomics. 285(5). 375–91. 1 indexed citations
6.
Cserző, Miklós, Gábor Turu, Péter Várnai, & László Hunyady. (2010). Relating underrepresented genomic DNA patterns and tiRNAs: the rule behind the observation and beyond. Biology Direct. 5(1). 56–56. 7 indexed citations
7.
Szekeres, Mária, Gábor Turu, Anna Orient, et al.. (2009). Mechanisms of angiotensin II-mediated regulation of aldosterone synthase expression in H295R human adrenocortical and rat adrenal glomerulosa cells. Molecular and Cellular Endocrinology. 302(2). 244–253. 27 indexed citations
8.
Cserző, Miklós, Frank Eisenhaber, Birgit Eisenhaber, & István Simon. (2003). TM or not TM: transmembrane protein prediction with low false positive rate using DAS-TMfilter. Bioinformatics. 20(1). 136–137. 81 indexed citations
9.
Cserző, Miklós, Frank Eisenhaber, Birgit Eisenhaber, & István Simon. (2002). On filtering false positive transmembrane protein predictions. Protein Engineering Design and Selection. 15(9). 745–752. 118 indexed citations
10.
Tompa, Péter, Gábor Tusnády, Miklós Cserző, & István Simon. (2001). Prion protein: Evolution caught en route. Proceedings of the National Academy of Sciences. 98(8). 4431–4436. 29 indexed citations
11.
Cserző, Miklós, Erik Jakob Wallin, István Simon, Gunnar von Heijne, & Arne Elofsson. (1997). Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method. Protein Engineering Design and Selection. 10(6). 673–676. 902 indexed citations breakdown →
12.
Cserző, Miklós, J.M. Bernassau, István Simon, & Bernard Maigret. (1994). New Alignment Strategy for Transmembrane Proteins. Journal of Molecular Biology. 243(3). 388–396. 43 indexed citations
13.
Pongor, Sándor, et al.. (1993). The SBASE domain library: a collection of annotated protein segments. Protein Engineering Design and Selection. 6(4). 391–395. 18 indexed citations
14.
Pongor, Sándor, et al.. (1993). The SBASE protein domain library, release 2.0: a collection of annotated protein sequence segments. Nucleic Acids Research. 21(13). 3111–3115. 16 indexed citations
15.
Simon, György, Rudolph D. Paladini, Sergio Tisminetzky, et al.. (1992). Improved detection of homology in distantly related proteins: similarity of adducin with actin-binding proteins.. PubMed. 5(1). 39–42. 6 indexed citations
16.
Fiser, András, et al.. (1992). Different sequence environments of cysteines and half cystines in proteins Application to predict disulfide forming residues. FEBS Letters. 302(2). 117–120. 55 indexed citations
17.
Cserző, Miklós & Tamás Vicsek. (1991). Self-affine fractal analysis of protein structures. Chaos Solitons & Fractals. 1(5). 431–438. 4 indexed citations
18.
Cserző, Miklós, et al.. (1990). Predicting isomorphic residue replacements for protein design. International journal of peptide & protein research. 36(3). 236–239. 31 indexed citations
19.
Cserző, Miklós & István Simon. (1989). Regularities in the primary structure of proteins. International journal of peptide & protein research. 34(3). 184–195. 26 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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