Jiekai Yu

1.9k total citations
73 papers, 1.5k citations indexed

About

Jiekai Yu is a scholar working on Molecular Biology, Spectroscopy and Oncology. According to data from OpenAlex, Jiekai Yu has authored 73 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Molecular Biology, 32 papers in Spectroscopy and 13 papers in Oncology. Recurrent topics in Jiekai Yu's work include Advanced Proteomics Techniques and Applications (30 papers), Metabolomics and Mass Spectrometry Studies (14 papers) and Mass Spectrometry Techniques and Applications (12 papers). Jiekai Yu is often cited by papers focused on Advanced Proteomics Techniques and Applications (30 papers), Metabolomics and Mass Spectrometry Studies (14 papers) and Mass Spectrometry Techniques and Applications (12 papers). Jiekai Yu collaborates with scholars based in China and United States. Jiekai Yu's co-authors include Shu Zheng, Suzhan Zhang, Yiding Chen, Cheng Guo, Xun Hu, Xiaofen Li, Qiuliang Liu, Ying Yuan, Fuquan Yang and Minfeng Ye and has published in prestigious journals such as Journal of Clinical Oncology, PLoS ONE and PEDIATRICS.

In The Last Decade

Jiekai Yu

73 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jiekai Yu China 21 789 410 255 240 163 73 1.5k
Feng Su United States 19 853 1.1× 298 0.7× 225 0.9× 555 2.3× 154 0.9× 39 1.9k
Fernando de la Cuesta Spain 25 899 1.1× 289 0.7× 82 0.3× 280 1.2× 144 0.9× 67 1.6k
Nicholas W. Bateman United States 21 837 1.1× 378 0.9× 246 1.0× 222 0.9× 96 0.6× 69 1.4k
Ji Gao China 18 685 0.9× 186 0.5× 164 0.6× 321 1.3× 85 0.5× 55 1.5k
Aleksandra Gentry‐Maharaj United Kingdom 27 1.1k 1.3× 196 0.5× 550 2.2× 622 2.6× 179 1.1× 86 2.5k
Fernando Gómez Spain 18 857 1.1× 95 0.2× 163 0.6× 234 1.0× 174 1.1× 56 1.5k
Philipp Schatz Germany 16 725 0.9× 115 0.3× 317 1.2× 337 1.4× 247 1.5× 24 1.2k
Denglong Wu China 24 805 1.0× 108 0.3× 152 0.6× 497 2.1× 312 1.9× 87 1.5k
Brian Piening United States 17 720 0.9× 321 0.8× 375 1.5× 127 0.5× 69 0.4× 59 1.5k
Dominique Könsgen Germany 18 645 0.8× 131 0.3× 274 1.1× 312 1.3× 89 0.5× 33 1.2k

Countries citing papers authored by Jiekai Yu

Since Specialization
Citations

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

Fields of papers citing papers by Jiekai Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jiekai Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Jiekai Yu. A scholar is included among the top collaborators of Jiekai Yu 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 Jiekai Yu. Jiekai Yu 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.
Zhao, Yingxin, Hong Shen, Jianmin Wu, et al.. (2024). Discovery, identification and mechanism of chemosensitivity-relate biomarker inter-α-trypsin inhibitor heavy chain 4 in metastatic colorectal cancer. Heliyon. 10(13). e33571–e33571. 2 indexed citations
2.
3.
Guo, Cheng, Qin Chen, Jiani Chen, et al.. (2019). 8-Hydroxyguanosine as a possible RNA oxidative modification marker in urine from colorectal cancer patients: Evaluation by ultra performance liquid chromatography-tandem mass spectrometry. Journal of Chromatography B. 1136. 121931–121931. 32 indexed citations
4.
Yu, Jiekai, Yanqin Huang, Xiaofen Li, et al.. (2018). Identification of Kininogen 1 as a Serum Protein Marker of Colorectal Adenoma in Patients with a Family History of Colorectal Cancer. Journal of Cancer. 9(3). 540–547. 13 indexed citations
5.
Yuan, Ruoshi, Suzhan Zhang, Jiekai Yu, et al.. (2017). Beyond cancer genes: colorectal cancer as robust intrinsic states formed by molecular interactions. Open Biology. 7(11). 10 indexed citations
6.
Zhou, Jiaojiao, Xuan Zhu, Meng Luo, et al.. (2017). Germline mutations of PALB2 gene in a sequential series of Chinese patients with breast cancer. Breast Cancer Research and Treatment. 166(3). 865–873. 19 indexed citations
7.
Guo, Cheng, Xiaofen Li, Rong Wang, et al.. (2016). Association between Oxidative DNA Damage and Risk of Colorectal Cancer: Sensitive Determination of Urinary 8-Hydroxy-2′-deoxyguanosine by UPLC-MS/MS Analysis. Scientific Reports. 6(1). 32581–32581. 116 indexed citations
8.
Yang, Shao‐Yu, et al.. (2016). Proteins associated with EGFR-TKIs resistance in patients with non-small cell lung cancer revealed by mass spectrometry. Molecular Medicine Reports. 14(5). 4823–4829. 4 indexed citations
9.
10.
Xu, Chengfu, Longgui Ning, Ming Yang, et al.. (2016). Exploration of Serum Proteomic Profiling and Diagnostic Model That Differentiate Crohn's Disease and Intestinal Tuberculosis. PLoS ONE. 11(12). e0167109–e0167109. 20 indexed citations
11.
Wang, Minmin, Jin Qiu, Hai‐Yan Tu, et al.. (2011). Detection of renal allograft dysfunction with characteristic protein fingerprint by serum proteomic analysis. International Urology and Nephrology. 43(4). 1009–1017. 5 indexed citations
12.
Yu, Chaohui, Chengfu Xu, Lei Xu, et al.. (2011). Serum proteomic analysis revealed diagnostic value of hemoglobin for nonalcoholic fatty liver disease. Journal of Hepatology. 56(1). 241–247. 80 indexed citations
13.
Wang, Jiaxiang, Lei Wang, Da Zhang, et al.. (2011). Identification of potential serum biomarkers for Wilms tumor after excluding confounding effects of common systemic inflammatory factors. Molecular Biology Reports. 39(5). 5095–5104. 17 indexed citations
14.
Wang, Jun, Jiaofang Shao, Fan Mo, et al.. (2010). Identification of Novel SNPs by Next-Generation Sequencing of the Genomic Region Containing the APC Gene in Colorectal Cancer Patients in China. OMICS A Journal of Integrative Biology. 14(3). 315–325. 6 indexed citations
15.
Qiu, Fuming, Jiekai Yu, Yiding Chen, et al.. (2009). Mining novel biomarkers for prognosis of gastric cancer with serum proteomics. Journal of Experimental & Clinical Cancer Research. 28(1). 126–126. 18 indexed citations
16.
Mei, Hong, Xiaoping Zhang, Yu Hu, et al.. (2008). The potential biomarkers for thromboembolism detected by SELDI-TOF-MS. Thrombosis Research. 123(3). 556–564. 17 indexed citations
17.
Zhang, Xiaoping, Tao Guo, Huafang Wang, et al.. (2008). Potential Biomarkers of Acute Cerebral Infarction Detected by SELDI-TOF-MS. American Journal of Clinical Pathology. 130(2). 299–304. 17 indexed citations
18.
Liu, Daren, Liping Cao, Jiekai Yu, et al.. (2008). Diagnosis of Pancreatic Adenocarcinoma Using Protein Chip Technology. Pancreatology. 9(1-2). 127–135. 18 indexed citations
19.
Hu, Yue, et al.. (2005). SELDI-TOF-MS: the proteomics and bioinformatics approaches in the diagnosis of breast cancer. The Breast. 14(4). 250–255. 79 indexed citations
20.
Yu, Jiekai, Shu Zheng, Yong Tang, & Li Li. (2005). An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer. Journal of Zhejiang University SCIENCE A. 6B(4). 227–231. 33 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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