Kinga Pajdzik

920 total citations · 1 hit paper
15 papers, 584 citations indexed

About

Kinga Pajdzik is a scholar working on Molecular Biology, Cancer Research and Pharmacology. According to data from OpenAlex, Kinga Pajdzik has authored 15 papers receiving a total of 584 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 6 papers in Cancer Research and 3 papers in Pharmacology. Recurrent topics in Kinga Pajdzik's work include RNA modifications and cancer (10 papers), Cancer-related molecular mechanisms research (5 papers) and Drug-Induced Adverse Reactions (3 papers). Kinga Pajdzik is often cited by papers focused on RNA modifications and cancer (10 papers), Cancer-related molecular mechanisms research (5 papers) and Drug-Induced Adverse Reactions (3 papers). Kinga Pajdzik collaborates with scholars based in United States, China and Poland. Kinga Pajdzik's co-authors include Qing Dai, Chuan He, Yangping Li, Yunhee Kang, Ranhui Duan, Andrew M. Shafik, Emily G. Allen, Peng Jin, Zhenxing Guo and Hao Wu and has published in prestigious journals such as Nature Communications, Nature Biotechnology and Nature Cell Biology.

In The Last Decade

Kinga Pajdzik

13 papers receiving 577 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Quantitative sequencing using BID-seq uncovers abundant pseudouridines in mammalian mRNA at base resolution

2022 144 citations Qing Dai, Lisheng Zhang et al. Nature Biotechnology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kinga Pajdzik United States	9	520	209	44	42	40	15	584
Santosh Timilsina United States	9	490 0.9×	243 1.2×	52 1.2×	9 0.2×	67 1.7×	22	566
Xingya Guo China	9	583 1.1×	404 1.9×	39 0.9×	11 0.3×	61 1.5×	14	660
Pengbo Zhang China	8	286 0.6×	195 0.9×	21 0.5×	9 0.2×	61 1.5×	11	336
Zaira Ianniello Italy	8	417 0.8×	191 0.9×	48 1.1×	5 0.1×	21 0.5×	10	435
Jinlin Du China	11	237 0.5×	140 0.7×	10 0.2×	8 0.2×	73 1.8×	19	344
Andrew M. Shafik United States	10	423 0.8×	220 1.1×	28 0.6×	13 0.3×	20 0.5×	15	466
Nassim E. Ajami United States	5	217 0.4×	116 0.6×	10 0.2×	28 0.7×	8 0.2×	5	319
Canfeng Zhang China	6	320 0.6×	117 0.6×	26 0.6×	31 0.7×	34 0.8×	10	360
Kuanxiang Sun China	10	274 0.5×	107 0.5×	16 0.4×	16 0.4×	15 0.4×	27	347
Kosuke Matsumura Japan	9	239 0.5×	148 0.7×	5 0.1×	12 0.3×	37 0.9×	11	308

Countries citing papers authored by Kinga Pajdzik

Since Specialization

Citations

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

Fields of papers citing papers by Kinga Pajdzik

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kinga Pajdzik

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

All Works

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15 of 15 papers shown

Zhao, Yutao, Hui‐Lung Sun, Wenlong Li, et al.. (2025). Pseudouridylation of 7SK by PUS7 regulates Pol II transcription elongation. Nature Communications. 16(1). 9595–9595.

Visioli, Fernanda, Mahnaz Bonrouhi, Kinga Pajdzik, et al.. (2024). The Role of SOX2 and SOX9 in Radioresistance and Tumor Recurrence. Cancers. 16(2). 439–439. 9 indexed citations

Pajdzik, Kinga, Ruitu Lyu, Xiaoyang Dou, et al.. (2024). Chemical manipulation of m¹A mediates its detection in human tRNA. RNA. 30(5). 548–559. 6 indexed citations

Zhao, Yutao, Xinyuan Ma, Chang Ye, et al.. (2024). Pseudouridine Detection and Quantification Using Bisulfite Incorporation Hindered Ligation. ACS Chemical Biology. 19(8). 1813–1819. 7 indexed citations

Dou, Xiaoyang, Yu Xiao, Chao Shen, et al.. (2023). RBFOX2 recognizes N6-methyladenosine to suppress transcription and block myeloid leukaemia differentiation. Nature Cell Biology. 25(9). 1359–1368. 52 indexed citations

Lyu, Ruitu, Kinga Pajdzik, Hui‐Lung Sun, et al.. (2023). A Quantitative Sequencing Method for 5‐Formylcytosine in RNA. Israel Journal of Chemistry. 64(3-4). e202300111–e202300111. 4 indexed citations

Dai, Qing, Lisheng Zhang, Hui‐Lung Sun, et al.. (2022). Quantitative sequencing using BID-seq uncovers abundant pseudouridines in mammalian mRNA at base resolution. Nature Biotechnology. 41(3). 344–354. 144 indexed citations breakdown →

Chokkalla, Anil K., Kinga Pajdzik, Xiaoyang Dou, et al.. (2022). Dysregulation of the Epitranscriptomic Mark m1A in Ischemic Stroke. Translational Stroke Research. 14(6). 806–810. 7 indexed citations

Shafik, Andrew M., Feiran Zhang, Zhenxing Guo, et al.. (2021). N6-methyladenosine dynamics in neurodevelopment and aging, and its potential role in Alzheimer’s disease. Genome biology. 22(1). 17–17. 181 indexed citations

10.

Zhao, Fanpeng, Ying Xu, Shichao Gao, et al.. (2021). METTL3-dependent RNA m6A dysregulation contributes to neurodegeneration in Alzheimer’s disease through aberrant cell cycle events. Molecular Neurodegeneration. 16(1). 70–70. 132 indexed citations

11.

Pajdzik, Kinga, Bogdan Jakieła, Izabela Kupryś‐Lipińska, et al.. (2021). Biomarkers for predicting response to aspirin therapy in aspirin‐exacerbated respiratory disease. Clinical & Experimental Allergy. 51(8). 1046–1056. 13 indexed citations

12.

Pajdzik, Kinga, et al.. (2020). Anterior gradient 2 promotes tumorigenesis through upregulation of CCAAT‐enhancer binding protein beta and hypoxia‐inducible factor‐2α and subsequent secretion of interleukin‐6, interleukin‐8, and vascular endothelial growth factor in the Caki‐1 clear cell renal cell carcinoma cell line. IUBMB Life. 72(8). 1807–1818. 9 indexed citations

13.

Pajdzik, Kinga, Ewa Konduracka, Adam Ćmiel, et al.. (2020). Artificial neural network identifies nonsteroidal anti‐inflammatory drugs exacerbated respiratory disease (N‐ERD) cohort. Allergy. 75(7). 1649–1658. 9 indexed citations

14.

Kupryś‐Lipińska, Izabela, Ewa Czarnobilska, Bogdan Jakieła, et al.. (2019). Sputum biomarkers during aspirin desensitization in nonsteroidal anti-inflammatory drugs exacerbated respiratory disease. Respiratory Medicine. 152. 51–59. 11 indexed citations

15.

Krawczyk, Katarzyna, et al.. (2016). The influence of heavy metals on Phaeodactylum tricornutum growth : a preliminary study. Homo Politicus (Academy of Humanities and Economics in Lodz).

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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