Jing Ye

1.6k total citations
49 papers, 1.2k citations indexed

About

Jing Ye is a scholar working on Endocrinology, Diabetes and Metabolism, Electrical and Electronic Engineering and Surgery. According to data from OpenAlex, Jing Ye has authored 49 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Endocrinology, Diabetes and Metabolism, 19 papers in Electrical and Electronic Engineering and 13 papers in Surgery. Recurrent topics in Jing Ye's work include Thyroid Cancer Diagnosis and Treatment (22 papers), Advanced battery technologies research (13 papers) and Thyroid and Parathyroid Surgery (11 papers). Jing Ye is often cited by papers focused on Thyroid Cancer Diagnosis and Treatment (22 papers), Advanced battery technologies research (13 papers) and Thyroid and Parathyroid Surgery (11 papers). Jing Ye collaborates with scholars based in China, United States and Macao. Jing Ye's co-authors include Xiaohong Zhang, Yu Zhao, Zhihui Niu, Jia‐Wei Feng, Yuebin Lian, Zhao Deng, Yang Peng, Hao Sun, Pengwei Qi and Gaole Dai and has published in prestigious journals such as Advanced Functional Materials, Journal of Power Sources and Scientific Reports.

In The Last Decade

Jing Ye

48 papers receiving 1.2k citations

Peers

Jing Ye

EEE

RESE

EDM

SURGE

Ruya Zhang China

Yasuhiro Seki Japan

Minqi Chen China

Feila Liu China

Chunyan Zeng China

Rong Zheng China

Mika Eguchi Japan

Zbigniew Rogulski Poland

Guanwen Wang China

Ruya Zhang China

Jing Ye

326 ×0.5

EEE

464 ×1.1

RESE

72 ×0.3

EDM

259 ×1.5

29 ×0.2

SURGE

Citations per year, relative to Jing Ye Jing Ye (= 1×) peers Ruya Zhang

Countries citing papers authored by Jing Ye

Since Specialization

Citations

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

Fields of papers citing papers by Jing Ye

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jing Ye

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

All Works

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20 of 20 papers shown

Feng, Jia‐Wei, Shuiqing Liu, Yuxin Yang, et al.. (2025). Neural Network and Logistic Regression Models Based on Ultrasound Radiomics and Clinical-Pathological Features to Predict Occult Level II Lymph Node Metastasis in Papillary Thyroid Carcinoma. Academic Radiology. 32(4). 1918–1933.

Chen, Tianyu, et al.. (2025). Prussian blue derived sulfides and phosphates with hollow nanoflower-like morphology realizing significant improvement of electrocatalytic water splitting performance. Materials Today Chemistry. 45. 102666–102666. 2 indexed citations

Luo, Min, Jiayi Wang, Liuzhen Wang, et al.. (2024). Li6PS5Cl/MoS2 hybrid electrolyte integrates high sulfur conversion kinetics with stable lithium metal interfaces in all-solid-state lithium-sulfur batteries. Nano Energy. 135. 110628–110628. 5 indexed citations

Wang, Lian, Yanghui Zhu, Nan Zhang, et al.. (2024). The multiple roles of interferon regulatory factor family in health and disease. Signal Transduction and Targeted Therapy. 9(1). 282–282. 34 indexed citations

Feng, Jia‐Wei, et al.. (2024). Preoperative Prediction of Occult Level V Lymph Node Metastasis in Papillary Thyroid Carcinoma: Development and Validation of a Radiomics-Driven Nomogram Model. Academic Radiology. 32(3). 1360–1372. 2 indexed citations

Ye, Jing, Xinyuan Bi, Shiyu Deng, et al.. (2024). Hypoxanthine is a metabolic biomarker for inducing GSDME-dependent pyroptosis of endothelial cells during ischemic stroke. Theranostics. 14(15). 6071–6087. 10 indexed citations

Feng, Jia‐Wei, et al.. (2024). Development and Validation of Clinical-Radiomics Nomogram for Preoperative Prediction of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma. Academic Radiology. 31(6). 2292–2305. 13 indexed citations

Feng, Jia‐Wei, et al.. (2023). Nomograms for Prediction of High‐Volume Lymph Node Metastasis in Papillary Thyroid Carcinoma Patients. Otolaryngology. 168(5). 1054–1066. 5 indexed citations

Feng, Jia‐Wei, et al.. (2023). Construction of prediction models for determining the risk of lateral lymph node metastasis in patients with thyroid papillary carcinoma based on gender stratification. European Archives of Oto-Rhino-Laryngology. 280(5). 2511–2523. 3 indexed citations

10.

Ye, Jing, et al.. (2022). Papillary Thyroid Microcarcinoma: A Nomogram Based on Clinical and Ultrasound Features to Improve the Prediction of Lymph Node Metastases in the Central Compartment. Frontiers in Endocrinology. 12. 770824–770824. 8 indexed citations

11.

Feng, Jia‐Wei, et al.. (2022). LASSO-based machine learning models for the prediction of central lymph node metastasis in clinically negative patients with papillary thyroid carcinoma. Frontiers in Endocrinology. 13. 1030045–1030045. 9 indexed citations

12.

Feng, Jia‐Wei, Jing Ye, Jun Hu, et al.. (2022). Nomograms for the prediction of lateral lymph node metastasis in papillary thyroid carcinoma: Stratification by size. Frontiers in Oncology. 12. 944414–944414. 14 indexed citations

13.

Feng, Jia‐Wei, et al.. (2022). A comparative analysis of eight machine learning models for the prediction of lateral lymph node metastasis in patients with papillary thyroid carcinoma. Frontiers in Endocrinology. 13. 11 indexed citations

14.

Feng, Jia‐Wei, Fei Wang, Jun Hu, et al.. (2021). A Nomogram Based on Clinical and Ultrasound Characteristics to Predict Central Lymph Node Metastasis of Papillary Thyroid Carcinoma. Frontiers in Endocrinology. 12. 666315–666315. 34 indexed citations

15.

Zhao, Hongbo, Chang Liu, Jing Ye, et al.. (2021). A comparison between deep learning convolutional neural networks and radiologists in the differentiation of benign and malignant thyroid nodules on CT images. Endokrynologia Polska. 72(3). 217–225. 16 indexed citations

16.

Feng, Jia‐Wei, Jing Ye, Shengyong Liu, et al.. (2021). Nomograms to predict ipsilateral and contralateral central lymph node metastasis in clinically lymph node-negative patients with solitary isthmic classic papillary thyroid carcinoma. Surgery. 170(6). 1670–1679. 6 indexed citations

17.

Feng, Jia‐Wei, Ancheng Qin, Jing Ye, et al.. (2019). Predictive Factors for Lateral Lymph Node Metastasis and Skip Metastasis in Papillary Thyroid Carcinoma. Endocrine Pathology. 31(1). 67–76. 49 indexed citations

18.

Feng, Jia‐Wei, Hua Pan, Lei Wang, et al.. (2019). Determine the Optimal Extent of Thyroidectomy and Lymphadenectomy for Patients With Papillary Thyroid Microcarcinoma. Frontiers in Endocrinology. 10. 363–363. 15 indexed citations

19.

Xu, Wenchao, Lingling Wang, Ying An, & Jing Ye. (2019). Expression of WD Repeat Domain 5 (WDR5) is Associated with Progression and Reduced Prognosis in Papillary Thyroid Carcinoma. Medical Science Monitor. 25. 3762–3770. 2 indexed citations

20.

Agrawal, Varun, Jing Ye, John McCann, et al.. (2010). Progressive renal failure in a patient with lung adenocarcinoma. Clinical Kidney Journal. 3(5). 461–464. 1 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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