Weijing Tang

783 total citations
19 papers, 325 citations indexed

About

Weijing Tang is a scholar working on Epidemiology, Hepatology and Health Information Management. According to data from OpenAlex, Weijing Tang has authored 19 papers receiving a total of 325 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Epidemiology, 4 papers in Hepatology and 3 papers in Health Information Management. Recurrent topics in Weijing Tang's work include Liver Disease Diagnosis and Treatment (3 papers), Artificial Intelligence in Healthcare (3 papers) and Hepatitis C virus research (3 papers). Weijing Tang is often cited by papers focused on Liver Disease Diagnosis and Treatment (3 papers), Artificial Intelligence in Healthcare (3 papers) and Hepatitis C virus research (3 papers). Weijing Tang collaborates with scholars based in United States, China and Singapore. Weijing Tang's co-authors include Amit G. Singal, Ji Zhu, Monica A. Tincopa, Akbar K. Waljee, Tony Van, Elliot B. Tapper, Lauren A. Beste, George N. Ioannou, Grace L. Su and Sameehan Mahajani and has published in prestigious journals such as Neuron, Journal of the American Statistical Association and Molecular Cell.

In The Last Decade

Weijing Tang

17 papers receiving 319 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Weijing Tang United States 10 101 71 70 49 42 19 325
Koji Todaka Japan 14 135 1.3× 43 0.6× 38 0.5× 26 0.5× 14 0.3× 34 500
Ichiro Ohsawa Japan 14 94 0.9× 91 1.3× 87 1.2× 43 0.9× 17 0.4× 32 449
Peipei Wang China 9 113 1.1× 154 2.2× 75 1.1× 50 1.0× 34 0.8× 25 398
Cong Chen China 14 395 3.9× 105 1.5× 46 0.7× 35 0.7× 17 0.4× 38 778
Ze Zhang China 8 307 3.0× 85 1.2× 38 0.5× 31 0.6× 134 3.2× 18 625
Ling Jia China 15 141 1.4× 142 2.0× 14 0.2× 33 0.7× 15 0.4× 34 484
Lin Wan China 13 280 2.8× 39 0.5× 14 0.2× 20 0.4× 18 0.4× 31 618
Lei Hu China 15 166 1.6× 48 0.7× 11 0.2× 61 1.2× 28 0.7× 59 543
Evgeny Minin Germany 12 89 0.9× 60 0.8× 99 1.4× 44 0.9× 3 0.1× 16 331
Yiqian Wang China 10 104 1.0× 121 1.7× 49 0.7× 24 0.5× 4 0.1× 21 352

Countries citing papers authored by Weijing Tang

Since Specialization
Citations

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

Fields of papers citing papers by Weijing Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Weijing Tang

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

All Works

19 of 19 papers shown
1.
2.
Tang, Weijing & Ji Zhu. (2024). Population-Level Balance in Signed Networks. Journal of the American Statistical Association. 120(550). 751–763.
3.
Yang, X. Jessie, Yi‐Hao Yen, Nega Berhe, et al.. (2024). Machine-learning methodologies to predict disease progression in chronic hepatitis B in Africa. Hepatology Communications. 8(12). 3 indexed citations
4.
Golbus, Jessica R., Thomas Cascino, Weijing Tang, et al.. (2023). Greater geographic sharing and heart transplantation waitlist outcomes following the 2018 heart allocation policy. The Journal of Heart and Lung Transplantation. 42(7). 936–942. 10 indexed citations
5.
Otero‐García, Marcos, Sameehan Mahajani, Debia Wakhloo, et al.. (2022). Molecular signatures underlying neurofibrillary tangle susceptibility in Alzheimer’s disease. Neuron. 110(18). 2929–2948.e8. 108 indexed citations
6.
Hughes, Nicholas W., Jiaqi Zhang, Weijing Tang, et al.. (2022). Machine-learning-optimized Cas12a barcoding enables the recovery of single-cell lineages and transcriptional profiles. Molecular Cell. 82(16). 3103–3118.e8. 16 indexed citations
7.
Tang, Weijing, Kevin He, Gongjun Xu, & Ji Zhu. (2022). Survival Analysis via Ordinary Differential Equations. Journal of the American Statistical Association. 118(544). 2406–2421. 2 indexed citations
8.
Tang, Weijing, et al.. (2021). Bounded multi-soliton solutions and their asymptotic analysis for the reversal-time nonlocal nonlinear Schrödinger equation. Communications in Theoretical Physics. 73(10). 105001–105001. 3 indexed citations
9.
Sun, Zepang, Yuming Jiang, Chuanli Chen, et al.. (2021). Radiomics signature based on computed tomography images for the preoperative prediction of lymph node metastasis at individual stations in gastric cancer: A multicenter study. Radiotherapy and Oncology. 165. 179–190. 20 indexed citations
10.
Zhou, Jian, Liang Zhou, Duo Zhang, et al.. (2020). BCL11A Promotes the Progression of Laryngeal Squamous Cell Carcinoma. Frontiers in Oncology. 10. 375–375. 15 indexed citations
11.
Tang, Weijing, Jiaqi Ma, Akbar K. Waljee, & Ji Zhu. (2020). Semi‐supervised joint learning for longitudinal clinical events classification using neural network models. Stat. 9(1). 1 indexed citations
12.
Ioannou, George N., Weijing Tang, Lauren A. Beste, et al.. (2020). Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis. JAMA Network Open. 3(9). e2015626–e2015626. 98 indexed citations
13.
Ma, Jiaqi, Weijing Tang, Ji Zhu, & Qiaozhu Mei. (2019). A Flexible Generative Framework for Graph-based Semi-supervised Learning. arXiv (Cornell University). 32. 3276–3285. 12 indexed citations
14.
Ioannou, George N., Weijing Tang, Lauren A. Beste, et al.. (2019). 498 – Deep Learning Models Accurately Predict Development of Hcc in 146,218 Patients with Chronic Hepatitis C. Gastroenterology. 156(6). S–1201. 1 indexed citations
15.
You, Ruxu, Xinyu Qian, Weijing Tang, et al.. (2018). Cost Effectiveness of Bosentan for Pulmonary Arterial Hypertension: A Systematic Review. Canadian Respiratory Journal. 2018. 1–12. 9 indexed citations
16.
Tang, Weijing, et al.. (2017). Role of T helper 17 cytokines in the tumour immune inflammation response of patients with laryngeal squamous cell carcinoma. Oncology Letters. 14(1). 561–568. 5 indexed citations
17.
Tang, Weijing, Xiaoling Shi, Wenping Li, et al.. (2016). Association of the microsatellite (GT)n repeat polymorphisms of the HO-1 gene promoter and corresponding serum levels with the risk of laryngeal squamous cell carcinoma. Acta Oto-Laryngologica. 136(8). 806–811. 9 indexed citations
18.
Tang, Weijing, et al.. (2016). The ratio of CD4/CD8 T-cells in human papillomavirus-positive laryngeal squamous cell carcinoma accounts for improved outcome. Acta Oto-Laryngologica. 136(8). 826–833. 12 indexed citations
19.
Lu, Jing, et al.. (2010). [The change of negative pressure in the cupping-cup and its influence on the depth of filiform-needle insertion].. PubMed. 27(1). 71–4. 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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