Uroš Kuzmanov

1.1k total citations
29 papers, 736 citations indexed

About

Uroš Kuzmanov is a scholar working on Molecular Biology, Cardiology and Cardiovascular Medicine and Oncology. According to data from OpenAlex, Uroš Kuzmanov has authored 29 papers receiving a total of 736 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 13 papers in Cardiology and Cardiovascular Medicine and 4 papers in Oncology. Recurrent topics in Uroš Kuzmanov's work include Cardiomyopathy and Myosin Studies (9 papers), Cardiac Fibrosis and Remodeling (7 papers) and Metabolomics and Mass Spectrometry Studies (3 papers). Uroš Kuzmanov is often cited by papers focused on Cardiomyopathy and Myosin Studies (9 papers), Cardiac Fibrosis and Remodeling (7 papers) and Metabolomics and Mass Spectrometry Studies (3 papers). Uroš Kuzmanov collaborates with scholars based in Canada, China and United States. Uroš Kuzmanov's co-authors include Andrew Emili, Eleftherios P. Diamandis, Hari Kosanam, Anthony O. Gramolini, Antoninus Soosaipillai, Christopher R. Smith, Nianxin Jiang, Hongbo Guo, Apostolos Dimitromanolakis and Karen E. Christensen and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Journal of Clinical Investigation.

In The Last Decade

Uroš Kuzmanov

28 papers receiving 731 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Uroš Kuzmanov Canada 16 472 113 77 75 66 29 736
Mattias Vesterlund Sweden 16 497 1.1× 118 1.0× 24 0.3× 125 1.7× 106 1.6× 29 829
Bamaprasad Dutta Singapore 15 588 1.2× 72 0.6× 20 0.3× 73 1.0× 155 2.3× 25 824
Vinothini Rajeeve United Kingdom 15 486 1.0× 95 0.8× 12 0.2× 146 1.9× 99 1.5× 37 823
Clement Chung United States 14 515 1.1× 238 2.1× 12 0.2× 168 2.2× 67 1.0× 49 955
Rong Hu China 18 533 1.1× 44 0.4× 22 0.3× 122 1.6× 102 1.5× 33 914
Markus Ruschhaupt Germany 13 436 0.9× 20 0.2× 93 1.2× 124 1.7× 80 1.2× 17 704
Xiuqin Xu China 13 740 1.6× 50 0.4× 48 0.6× 36 0.5× 12 0.2× 17 986
Lulu Cao China 19 533 1.1× 107 0.9× 13 0.2× 195 2.6× 155 2.3× 73 1.0k
Andy H. Vo United States 15 511 1.1× 17 0.2× 84 1.1× 24 0.3× 53 0.8× 26 793

Countries citing papers authored by Uroš Kuzmanov

Since Specialization
Citations

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

Fields of papers citing papers by Uroš Kuzmanov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Uroš Kuzmanov

This figure shows the co-authorship network connecting the top 25 collaborators of Uroš Kuzmanov. A scholar is included among the top collaborators of Uroš Kuzmanov 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 Uroš Kuzmanov. Uroš Kuzmanov 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.
Brahmbhatt, Darshan H., Fernando Luís Scolari, Patrick R. Lawler, et al.. (2025). Heart Failure Decompensation with Cardiogenic Shock Exhibits Distinct Sequential Inflammatory Profiles. ESC Heart Failure. 12(3). 2077–2086. 3 indexed citations
2.
Yerra, Veera Ganesh, Sri Nagarjun Batchu, Harmandeep Kaur, et al.. (2023). Pressure overload induces ISG15 to facilitate adverse ventricular remodeling and promote heart failure. Journal of Clinical Investigation. 133(9). 14 indexed citations
3.
Reitz, Cristine J., Da Hye Kim, Saumya Shah, et al.. (2023). Proteomics and phosphoproteomics of failing human left ventricle identifies dilated cardiomyopathy-associated phosphorylation of CTNNA3. Proceedings of the National Academy of Sciences. 120(19). e2212118120–e2212118120. 14 indexed citations
4.
Dufour, Catherine R., Hui Xia, Wafa B'Chir, et al.. (2022). Integrated multi-omics analysis of adverse cardiac remodeling and metabolic inflexibility upon ErbB2 and ERRα deficiency. Communications Biology. 5(1). 955–955. 1 indexed citations
5.
Wang, Erika Yan, Uroš Kuzmanov, Wenkun Dou, et al.. (2021). An organ-on-a-chip model for pre-clinical drug evaluation in progressive non-genetic cardiomyopathy. Journal of Molecular and Cellular Cardiology. 160. 97–110. 33 indexed citations
6.
Kuzmanov, Uroš, Erika Yan Wang, Rachel D. Vanderlaan, et al.. (2020). Mapping signalling perturbations in myocardial fibrosis via the integrative phosphoproteomic profiling of tissue from diverse sources. Nature Biomedical Engineering. 4(9). 889–900. 26 indexed citations
7.
8.
Goebels, Florian, Uroš Kuzmanov, Cuihong Wan, et al.. (2019). EPIC: software toolkit for elution profile-based inference of protein complexes. Nature Methods. 16(8). 737–742. 64 indexed citations
9.
Kuzmanov, Uroš, Neal I. Callaghan, Scott P. Heximer, et al.. (2019). Nanoscale reorganization of sarcoplasmic reticulum in pressure-overload cardiac hypertrophy visualized by dSTORM. Scientific Reports. 9(1). 7867–7867. 16 indexed citations
10.
Ma, Hongyue, Jing Zhou, Hongbo Guo, et al.. (2018). A strategy for the metabolomics-based screening of active constituents and quality consistency control for natural medicinal substance toad venom. Analytica Chimica Acta. 1031. 108–118. 12 indexed citations
11.
Kuzmanov, Uroš, et al.. (2016). Avoiding false discovery in biomarker research. BMC Biochemistry. 17(1). 17–17. 4 indexed citations
12.
Kuzmanov, Uroš, Hari Kosanam, & Eleftherios P. Diamandis. (2013). The sweet and sour of serological glycoprotein tumor biomarker quantification. BMC Medicine. 11(1). 31–31. 62 indexed citations
13.
Kuzmanov, Uroš & Andrew Emili. (2013). Protein-protein interaction networks: probing disease mechanisms using model systems. Genome Medicine. 5(4). 37–37. 121 indexed citations
14.
Kuzmanov, Uroš, et al.. (2013). Fuzzy decision support system for ship lock control. Expert Systems with Applications. 40(10). 3953–3960. 37 indexed citations
15.
Begcevic, Ilijana, Hari Kosanam, Eduardo Martínez‐Morillo, et al.. (2013). Semiquantitative proteomic analysis of human hippocampal tissues from Alzheimer’s disease and age-matched control brains. Clinical Proteomics. 10(1). 5–5. 49 indexed citations
16.
Kuzmanov, Uroš, Natasha Musrap, Hari Kosanam, et al.. (2012). Glycoproteomic identification of potential glycoprotein biomarkers in ovarian cancer proximal fluids. Clinical Chemistry and Laboratory Medicine (CCLM). 51(7). 1467–76. 26 indexed citations
17.
Bayani, Jane, Uroš Kuzmanov, Punit Saraon, et al.. (2012). Copy Number and Expression Alterations of miRNAs in the Ovarian Cancer Cell Line OVCAR-3: Impact on Kallikrein 6 Protein Expression. Clinical Chemistry. 59(1). 296–305. 18 indexed citations
18.
19.
Kuzmanov, Uroš, Nianxin Jiang, Christopher R. Smith, Antoninus Soosaipillai, & Eleftherios P. Diamandis. (2008). Differential N-glycosylation of Kallikrein 6 Derived from Ovarian Cancer Cells or the Central Nervous System. Molecular & Cellular Proteomics. 8(4). 791–798. 56 indexed citations
20.
Christensen, Karen E., et al.. (2004). Disruption of the Mthfd1 Gene Reveals a Monofunctional 10-Formyltetrahydrofolate Synthetase in Mammalian Mitochondria. Journal of Biological Chemistry. 280(9). 7597–7602. 41 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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