Yunjun Wang

2.1k total citations · 1 hit paper
47 papers, 1.5k citations indexed

About

Yunjun Wang is a scholar working on Molecular Biology, Endocrinology, Diabetes and Metabolism and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Yunjun Wang has authored 47 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 18 papers in Endocrinology, Diabetes and Metabolism and 9 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Yunjun Wang's work include Thyroid Cancer Diagnosis and Treatment (17 papers), Thyroid and Parathyroid Surgery (7 papers) and Protein Structure and Dynamics (6 papers). Yunjun Wang is often cited by papers focused on Thyroid Cancer Diagnosis and Treatment (17 papers), Thyroid and Parathyroid Surgery (7 papers) and Protein Structure and Dynamics (6 papers). Yunjun Wang collaborates with scholars based in China, Canada and United States. Yunjun Wang's co-authors include Oleg Jardetzky, Jun Xiang, Qing Guan, Duanshu Li, Hongtao Lu, David S. Wishart, Jiajun Du, Qin Yu, Xiaochun Wan and Bo Ping and has published in prestigious journals such as Journal of the American Chemical Society, Analytical Chemistry and Biochemistry.

In The Last Decade

Yunjun Wang

45 papers receiving 1.4k citations

Hit Papers

HIF-1α drives resistance to ferroptosis in solid tumors b... 2023 2026 2024 2025 2023 40 80 120

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yunjun Wang China 17 727 260 258 223 203 47 1.5k
Lingyun Huang China 21 459 0.6× 149 0.6× 318 1.2× 32 0.1× 163 0.8× 80 1.2k
J. Young United Kingdom 23 1.2k 1.6× 446 1.7× 74 0.3× 42 0.2× 37 0.2× 57 2.5k
Simone Brandt Switzerland 19 1.2k 1.7× 255 1.0× 135 0.5× 24 0.1× 40 0.2× 51 2.3k
Jesse G. Meyer United States 22 1.1k 1.6× 512 2.0× 30 0.1× 136 0.6× 71 0.3× 49 1.9k
Vito Riccardo Tomaso Zanotelli Switzerland 14 2.2k 3.1× 496 1.9× 108 0.4× 79 0.4× 36 0.2× 17 3.6k
Tiannan Guo China 28 1.8k 2.4× 1.1k 4.1× 104 0.4× 40 0.2× 38 0.2× 116 2.6k
Caroline Kampf Sweden 30 2.9k 3.9× 370 1.4× 453 1.8× 127 0.6× 24 0.1× 43 3.9k
Ling Wei China 24 399 0.5× 17 0.1× 574 2.2× 72 0.3× 105 0.5× 95 1.6k
Benjamin Balluff Netherlands 33 2.2k 3.1× 2.1k 8.1× 119 0.5× 78 0.3× 88 0.4× 78 3.4k
Tarek G. Gharib United States 10 2.3k 3.1× 378 1.5× 157 0.6× 57 0.3× 34 0.2× 10 3.2k

Countries citing papers authored by Yunjun Wang

Since Specialization
Citations

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

Fields of papers citing papers by Yunjun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yunjun Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Yunjun Wang. A scholar is included among the top collaborators of Yunjun Wang 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 Yunjun Wang. Yunjun Wang 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.
Yang, Yan, Ying Yin, Yuan Gao, et al.. (2024). Clinical significance of Cyclin D1 by complete quantification detection in mantle cell lymphoma: positive indicator in prognosis. Diagnostic Pathology. 19(1). 149–149. 5 indexed citations
2.
Wang, Yunjun & Zhiyuan Ren. (2024). Exploring the application of automatic distance measurement for standing long jump based on image denoising and area detection. Intelligent Decision Technologies. 18(4). 2977–2992. 1 indexed citations
3.
Zhang, Yu, Wei Su, Zhou Yang, et al.. (2024). iPLA2β regulates the dual effects of arachidonic acid in thyroid cancer. Head & Neck. 47(2). 504–516. 1 indexed citations
4.
Sang, Youzhou, Weibo Xu, Wanlin Liu, et al.. (2024). Targeting CDK2 Confers Vulnerability to Lenvatinib Via Driving Senescence in Anaplastic Thyroid Cancer. Advanced Science. 12(7). e2413514–e2413514. 3 indexed citations
5.
Qian, Kai, Yunjun Wang, Ning An, et al.. (2024). Effect and Safety of Apatinib as Neoadjuvant Therapy in Locally Advanced Differentiated Thyroid Cancer: A Phase 2 Trial. Journal of the Endocrine Society. 8(9). bvae132–bvae132. 3 indexed citations
7.
Hao, Qi, Xiaoni Liu, Guoxing He, et al.. (2024). Hyperspectral Remote Sensing Combined with Ground Vegetation Surveys for the Study of the Age of Rodent Mounds. Agriculture. 14(12). 2142–2142. 1 indexed citations
8.
Xiang, Jun, et al.. (2019). Risk Factors for Predicting Lymph Nodes Posterior to Right Recurrent Laryngeal Nerve (LN-prRLN) Metastasis in Thyroid Papillary Carcinoma: A Meta-Analysis. International Journal of Endocrinology. 2019. 1–11. 17 indexed citations
10.
Guan, Qing, Xiaochun Wan, Hongtao Lu, et al.. (2019). Deep convolutional neural network Inception-v3 model for differential diagnosing of lymph node in cytological images: a pilot study. Annals of Translational Medicine. 7(14). 307–307. 42 indexed citations
11.
Chen, Lili, Kai Qian, Kai Guo, et al.. (2019). A Novel N Staging System for Predicting Survival in Patients with Medullary Thyroid Cancer. Annals of Surgical Oncology. 26(13). 4430–4438. 11 indexed citations
13.
Guan, Qing, Yunjun Wang, Bo Ping, et al.. (2019). Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. Journal of Cancer. 10(20). 4876–4882. 191 indexed citations
14.
Guan, Qing, Yunjun Wang, Jiajun Du, et al.. (2019). Deep learning based classification of ultrasound images for thyroid nodules: a large scale of pilot study. Annals of Translational Medicine. 7(7). 137–137. 60 indexed citations
15.
Wang, Yunjun, Qing Guan, Jun Xiang, & Duanshu Li. (2018). Clinicopathologic features and prognostic factors of diffuse sclerosing variant of papillary thyroid carcinoma: a populationbased analysis. Translational Cancer Research. 7(3). 695–705. 3 indexed citations
16.
Wang, Yunjun, Qing Guan, & Jun Xiang. (2018). Nomogram for predicting central lymph node metastasis in papillary thyroid microcarcinoma: A retrospective cohort study of 8668 patients. International Journal of Surgery. 55. 98–102. 65 indexed citations
17.
Xiang, Jun, Xuemei Wang, Zhuoying Wang, et al.. (2014). Effect of Different Iodine Concentrations on Well-Differentiated Thyroid Cancer Cell Behavior and its Inner Mechanism. Cell Biochemistry and Biophysics. 71(1). 299–305. 14 indexed citations
18.
Wang, Yunjun, et al.. (2004). Solution structures of reduced and oxidized bacteriophage T4 glutaredoxin. Journal of Biomolecular NMR. 29(1). 85–90. 10 indexed citations
19.
Wang, Yunjun & Oleg Jardetzky. (2002). Probability‐based protein secondary structure identification using combined NMR chemical‐shift data. Protein Science. 11(4). 852–861. 361 indexed citations
20.
Wang, Yunjun, Shimin Zhao, Ronald L. Somerville, & Oleg Jardetzky. (2001). Solution structure of the DNA‐binding domain of the TyrR protein of Haemophilus influenzae. Protein Science. 10(3). 592–598. 16 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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