Martin Trbušek

2.2k total citations
51 papers, 1.2k citations indexed

About

Martin Trbušek is a scholar working on Genetics, Oncology and Pathology and Forensic Medicine. According to data from OpenAlex, Martin Trbušek has authored 51 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Genetics, 24 papers in Oncology and 21 papers in Pathology and Forensic Medicine. Recurrent topics in Martin Trbušek's work include Chronic Lymphocytic Leukemia Research (38 papers), Cancer-related Molecular Pathways (23 papers) and Lymphoma Diagnosis and Treatment (21 papers). Martin Trbušek is often cited by papers focused on Chronic Lymphocytic Leukemia Research (38 papers), Cancer-related Molecular Pathways (23 papers) and Lymphoma Diagnosis and Treatment (21 papers). Martin Trbušek collaborates with scholars based in Czechia, Germany and United Kingdom. Martin Trbušek's co-authors include Šárka Pospı́šilová, Jitka Malčíková, Jiřı́ Mayer, Michael Doubek, Yvona Brychtová, Boris Tichý, Marek Mráz, Jana Šmardová, Hana Skuhrová Francová and Šárka Pavlová and has published in prestigious journals such as Journal of Clinical Oncology, Blood and Cellular and Molecular Life Sciences.

In The Last Decade

Martin Trbušek

51 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Martin Trbušek Czechia 18 676 484 435 360 346 51 1.2k
Jana Šmardová Czechia 14 352 0.5× 296 0.6× 460 1.1× 431 1.2× 250 0.7× 57 1.0k
Johannes Coy Germany 10 490 0.7× 356 0.7× 332 0.8× 221 0.6× 225 0.7× 12 912
Benjamin L. Lampson United States 15 475 0.7× 348 0.7× 416 1.0× 205 0.6× 215 0.6× 30 942
Renata Walewska United Kingdom 18 464 0.7× 299 0.6× 660 1.5× 245 0.7× 314 0.9× 37 1.1k
Stefania Gobessi Italy 17 959 1.4× 499 1.0× 704 1.6× 245 0.7× 753 2.2× 34 1.6k
Simona Tavolaro Italy 16 384 0.6× 282 0.6× 401 0.9× 141 0.4× 221 0.6× 31 820
Serena Matis Italy 17 318 0.5× 215 0.4× 351 0.8× 180 0.5× 252 0.7× 36 767
Meaghan Wall Australia 23 276 0.4× 232 0.5× 983 2.3× 330 0.9× 204 0.6× 57 1.6k
Michael Grau Germany 17 274 0.4× 394 0.8× 597 1.4× 417 1.2× 496 1.4× 30 1.3k
Susanne Hipp Germany 15 378 0.6× 220 0.5× 717 1.6× 459 1.3× 298 0.9× 22 1.2k

Countries citing papers authored by Martin Trbušek

Since Specialization
Citations

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

Fields of papers citing papers by Martin Trbušek

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Martin Trbušek

This figure shows the co-authorship network connecting the top 25 collaborators of Martin Trbušek. A scholar is included among the top collaborators of Martin Trbušek 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 Martin Trbušek. Martin Trbušek 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.
Plevová, Karla, Šárka Pavlová, Jitka Malčíková, et al.. (2022). Evolution of TP53 abnormalities during CLL disease course is associated with telomere length changes. BMC Cancer. 22(1). 137–137. 3 indexed citations
2.
Trbušek, Martin, et al.. (2020). The Important Role of STAT3 in Chronic Lymphocytic Leukaemia Biology. Klinicka onkologie. 33(1). 32–38. 7 indexed citations
3.
Pavlová, Šárka, et al.. (2019). PRIMA-1MET cytotoxic effect correlates with p53 protein reduction in TP53-mutated chronic lymphocytic leukemia cells. Leukemia Research. 89. 106288–106288. 6 indexed citations
4.
Macháčková, Eva, Kathleen Claes, Petra Vašíčková, et al.. (2019). Twenty Years of BRCA1 and BRCA2 Molecular Analysis at MMCI – Current Developments for the Classifi cation of Variants. Klinicka onkologie. 32(Suppl 2). 51–71. 11 indexed citations
5.
Khirsariya, Prashant, Marek Borský, Jan Verner, et al.. (2019). Novel CHK1 inhibitor MU380 exhibits significant single-agent activity in TP53-mutated chronic lymphocytic leukemia cells. Haematologica. 104(12). 2443–2455. 22 indexed citations
6.
Černá, Kateřina Amruz, Jan Oppelt, Václav Šeda, et al.. (2018). MicroRNA miR-34a downregulates FOXP1 during DNA damage response to limit BCR signalling in chronic lymphocytic leukaemia B cells. Leukemia. 33(2). 403–414. 42 indexed citations
7.
Raa, Doreen te, Perry D. Moerland, Ingrid A. M. Derks, et al.. (2015). Assessment of p53 and ATM Functionality in Chronic Lymphocytic Leukemia By Multiplex Ligation-Dependent Probe Amplification. Blood. 126(23). 2918–2918. 1 indexed citations
8.
Raa, G. Doreen te, Perry D. Moerland, Nadja Laddach, et al.. (2015). Assessment of p53 and ATM functionality in chronic lymphocytic leukemia by multiplex ligation-dependent probe amplification. Cell Death and Disease. 6(8). e1852–e1852. 13 indexed citations
9.
Malčíková, Jitka, Kateřina Staňo Kozubík, Boris Tichý, et al.. (2014). Detailed analysis of therapy-driven clonal evolution of TP53 mutations in chronic lymphocytic leukemia. Leukemia. 29(4). 877–885. 106 indexed citations
10.
Raa, G. Doreen te, Veronika Navrkalová, Anna Skowrońska, et al.. (2014). The impact of SF3B1 mutations in CLL on the DNA-damage response. Leukemia. 29(5). 1133–1142. 61 indexed citations
11.
Navrkalová, Veronika, Jana Kmínková, Jitka Malčíková, et al.. (2013). ATM mutations uniformly lead to ATM dysfunction in chronic lymphocytic leukemia: application of functional test using doxorubicin. Haematologica. 98(7). 1124–1131. 26 indexed citations
12.
Smolej, Lukáš, Daniel Lysák, Yvona Brychtová, et al.. (2013). The outcome of chronic lymphocytic leukemia patients who relapsed after fludarabine, cyclophosphamide, and rituximab. European Journal Of Haematology. 90(6). 479–485. 9 indexed citations
13.
14.
Pospı́šilová, Šárka, David González, Jitka Malčíková, et al.. (2012). ERIC recommendations on TP53 mutation analysis in chronic lymphocytic leukemia. Leukemia. 26(7). 1458–1461. 133 indexed citations
15.
16.
Malčíková, Jitka, Jana Šmardová, Šoňa Peková, et al.. (2007). Identification of somatic hypermutations in the TP53 gene in B-cell chronic lymphocytic leukemia. Molecular Immunology. 45(5). 1525–1529. 10 indexed citations
17.
Havliš, Jan & Martin Trbušek. (2002). 5-Methylcytosine as a marker for the monitoring of DNA methylation. Journal of Chromatography B. 781(1-2). 373–392. 31 indexed citations
18.
Trbušek, Martin, et al.. (2001). Galactosemia: deletion in the 5′ upstream region of the GALT gene reduces promoter efficiency. Human Genetics. 109(1). 117–120. 19 indexed citations
19.
Trbušek, Martin, et al.. (2001). Identification of three novel mutations in the PHKA2 gene in Czech patients with X‐linked liver glycogenosis. Journal of Inherited Metabolic Disease. 24(1). 85–87. 12 indexed citations
20.
Uldrijan, Stjepan, et al.. (2001). Potent induction of wild-type p53-dependent transcription in tumour cells by a synthetic inhibitor of cyclin-dependent kinases. Cellular and Molecular Life Sciences. 58(9). 1333–1339. 42 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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