Vlatka Zoldoš

3.6k total citations · 1 hit paper
50 papers, 1.9k citations indexed

About

Vlatka Zoldoš is a scholar working on Molecular Biology, Plant Science and Immunology. According to data from OpenAlex, Vlatka Zoldoš has authored 50 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Molecular Biology, 14 papers in Plant Science and 10 papers in Immunology. Recurrent topics in Vlatka Zoldoš's work include Glycosylation and Glycoproteins Research (22 papers), Chromosomal and Genetic Variations (10 papers) and Plant Disease Resistance and Genetics (7 papers). Vlatka Zoldoš is often cited by papers focused on Glycosylation and Glycoproteins Research (22 papers), Chromosomal and Genetic Variations (10 papers) and Plant Disease Resistance and Genetics (7 papers). Vlatka Zoldoš collaborates with scholars based in Croatia, United States and France. Vlatka Zoldoš's co-authors include Aleksandar Vojta, Gordan Lauc, Marija Klasić, Vanja Tadić, Luka Bočkor, Petra Korać, Paula Dobrinić, Boris Jülg, Dražena Papeš and Sonja Šiljak-Yakovlev and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and Scientific Reports.

In The Last Decade

Vlatka Zoldoš

49 papers receiving 1.8k citations

Hit Papers

Repurposing the CRISPR-Cas9 system for targeted DNA methy... 2016 2026 2019 2022 2016 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vlatka Zoldoš Croatia 21 1.5k 408 347 301 165 50 1.9k
Cristina Chiva Spain 24 1.1k 0.7× 181 0.4× 122 0.4× 152 0.5× 33 0.2× 47 1.8k
Daniel Christophe Belgium 27 1.4k 0.9× 181 0.4× 603 1.7× 203 0.7× 40 0.2× 82 2.3k
Christopher J. Thorpe United Kingdom 21 1.3k 0.9× 163 0.4× 198 0.6× 501 1.7× 177 1.1× 38 2.2k
J L Slightom United States 33 2.3k 1.6× 1.4k 3.4× 474 1.4× 166 0.6× 64 0.4× 52 3.2k
Hideaki Abe Japan 18 521 0.4× 187 0.5× 233 0.7× 210 0.7× 34 0.2× 58 1.2k
Sharon M. Gorski Canada 29 1.6k 1.1× 105 0.3× 181 0.5× 247 0.8× 30 0.2× 67 2.6k
Yuanzheng He China 21 925 0.6× 723 1.8× 278 0.8× 121 0.4× 80 0.5× 43 1.8k
Rika Suzuki Japan 18 1.6k 1.1× 141 0.3× 696 2.0× 163 0.5× 31 0.2× 57 2.3k
Kazuo Yamagata Japan 34 2.4k 1.6× 194 0.5× 978 2.8× 322 1.1× 54 0.3× 99 3.9k
Miguel Godinho Ferreira Portugal 26 1.6k 1.1× 233 0.6× 276 0.8× 141 0.5× 18 0.1× 54 2.4k

Countries citing papers authored by Vlatka Zoldoš

Since Specialization
Citations

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

Fields of papers citing papers by Vlatka Zoldoš

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vlatka Zoldoš

This figure shows the co-authorship network connecting the top 25 collaborators of Vlatka Zoldoš. A scholar is included among the top collaborators of Vlatka Zoldoš 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 Vlatka Zoldoš. Vlatka Zoldoš 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.
Vujić, Ana, Marija Klasić, Gordan Lauc, et al.. (2024). Predicting Biochemical and Physiological Parameters: Deep Learning from IgG Glycome Composition. International Journal of Molecular Sciences. 25(18). 9988–9988. 1 indexed citations
2.
Krištić, Jasminka, et al.. (2023). Long-Term Culturing of FreeStyle 293-F Cells Affects Immunoglobulin G Glycome Composition. Biomolecules. 13(8). 1245–1245. 1 indexed citations
3.
Klasić, Marija, et al.. (2023). Transcription Factors HNF1A, HNF4A, and FOXA2 Regulate Hepatic Cell Protein N-Glycosylation. Engineering. 32. 57–68. 2 indexed citations
4.
Krištić, Jasminka, et al.. (2022). A Transient Expression System with Stably Integrated CRISPR-dCas9 Fusions for Regulation of Genes Involved in Immunoglobulin G Glycosylation. The CRISPR Journal. 5(2). 237–253. 7 indexed citations
5.
Tudor, Lucija, Marcela Konjevod, Gordana Nedić Erjavec, et al.. (2022). Genetic and Epigenetic Association of Hepatocyte Nuclear Factor-1α with Glycosylation in Post-Traumatic Stress Disorder. Genes. 13(6). 1063–1063. 2 indexed citations
6.
Pučić‐Baković, Maja, et al.. (2022). Heritability of the glycan clock of biological age. Frontiers in Cell and Developmental Biology. 10. 982609–982609. 6 indexed citations
7.
Kohrt, Wendy M., Jasminka Krištić, Domagoj Kifer, et al.. (2021). Effects of Estradiol on Immunoglobulin G Glycosylation: Mapping of the Downstream Signaling Mechanism. Frontiers in Immunology. 12. 680227–680227. 25 indexed citations
8.
Tadić, Vanja, Marija Klasić, Ivona Bečeheli, et al.. (2019). Antagonistic and synergistic epigenetic modulation using orthologous CRISPR/dCas9-based modular system. Nucleic Acids Research. 47(18). 9637–9657. 42 indexed citations
9.
Zoldoš, Vlatka, et al.. (2019). Active fusions of Cas9 orthologs. Journal of Biotechnology. 301. 18–23. 12 indexed citations
10.
Markulin, Dora, Aleksandar Vojta, Ivana Samaržija, et al.. (2017). Association Between RASSF1A Promoter Methylation and Testicular Germ Cell Tumor: A Meta-analysis and a Cohort Study. Cancer Genomics & Proteomics. 14(5). 363–372. 18 indexed citations
11.
Šiljak-Yakovlev, Sonja, Bernard Godelle, Vlatka Zoldoš, et al.. (2017). Evolutionary implications of heterochromatin and rDNA in chromosome number and genome size changes during dysploidy: A case study in Reichardia genus. PLoS ONE. 12(8). e0182318–e0182318. 20 indexed citations
12.
Vojta, Aleksandar, Paula Dobrinić, Vanja Tadić, et al.. (2016). Repurposing the CRISPR-Cas9 system for targeted DNA methylation. Nucleic Acids Research. 44(12). 5615–5628. 554 indexed citations breakdown →
13.
Klasić, Marija, Jasminka Krištić, Petra Korać, et al.. (2016). DNA hypomethylation upregulates expression of the MGAT3 gene in HepG2 cells and leads to changes in N-glycosylation of secreted glycoproteins. Scientific Reports. 6(1). 24363–24363. 22 indexed citations
14.
Zoldoš, Vlatka, Mislav Novokmet, Ivona Bečeheli, & Gordan Lauc. (2012). Genomics and epigenomics of the human glycome. Glycoconjugate Journal. 30(1). 41–50. 40 indexed citations
15.
Zoldoš, Vlatka, Tomislav Horvat, & Gordan Lauc. (2012). Glycomics meets genomics, epigenomics and other high throughput omics for system biology studies. Current Opinion in Chemical Biology. 17(1). 34–40. 46 indexed citations
16.
Zoldoš, Vlatka, Srđana Grgurević, & Gordan Lauc. (2010). Epigenetic regulation of protein glycosylation. BioMolecular Concepts. 1(3-4). 253–261. 17 indexed citations
17.
Lauc, Gordan & Vlatka Zoldoš. (2010). Protein glycosylation—an evolutionary crossroad between genes and environment. Molecular BioSystems. 6(12). 2373–2379. 32 indexed citations
18.
Lauc, Gordan & Vlatka Zoldoš. (2009). Epigenetic regulation of glycosylation could be a mechanism used by complex organisms to compete with microbes on an evolutionary scale. Medical Hypotheses. 73(4). 510–512. 12 indexed citations
19.
Bauer, Nataša, et al.. (2008). Nucleotide sequence, structural organization and length heterogeneity of ribosomal DNA intergenic spacer in Quercus petraea (Matt.) Liebl. and Q. robur L.. Molecular Genetics and Genomics. 281(2). 207–221. 15 indexed citations
20.
Zoldoš, Vlatka, et al.. (1997). Cytogenetic damages as an indicator of pedunculate oak forest decline. 4 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026