Weiying Gu

572 total citations
35 papers, 247 citations indexed

About

Weiying Gu is a scholar working on Hematology, Molecular Biology and Oncology. According to data from OpenAlex, Weiying Gu has authored 35 papers receiving a total of 247 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Hematology, 14 papers in Molecular Biology and 12 papers in Oncology. Recurrent topics in Weiying Gu's work include Multiple Myeloma Research and Treatments (10 papers), Lymphoma Diagnosis and Treatment (10 papers) and Immunotherapy and Immune Responses (4 papers). Weiying Gu is often cited by papers focused on Multiple Myeloma Research and Treatments (10 papers), Lymphoma Diagnosis and Treatment (10 papers) and Immunotherapy and Immune Responses (4 papers). Weiying Gu collaborates with scholars based in China, United States and Taiwan. Weiying Gu's co-authors include Xiaobao Xie, Jingting Jiang, Zhuojun Zheng, Jiannong Cen, Yuqing Miao, Ziyuan Shen, Wei Sang, Shuiping Huang, Guoqiang Qiu and Chenlu He and has published in prestigious journals such as Cancer, International Journal of Radiation Oncology*Biology*Physics and International Journal of Cancer.

In The Last Decade

Weiying Gu

30 papers receiving 246 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Weiying Gu China 10 95 76 58 58 55 35 247
Jiadai Xu China 11 143 1.5× 83 1.1× 107 1.8× 54 0.9× 27 0.5× 43 288
Nadia Harun Australia 6 80 0.8× 78 1.0× 64 1.1× 70 1.2× 60 1.1× 6 334
Sichu Liu China 11 61 0.6× 78 1.0× 40 0.7× 67 1.2× 93 1.7× 25 251
Yusuke Shiozawa Japan 9 107 1.1× 45 0.6× 82 1.4× 66 1.1× 56 1.0× 19 258
Yingwan Luo China 8 79 0.8× 46 0.6× 113 1.9× 20 0.3× 35 0.6× 29 244
Roberta Gonella Italy 10 61 0.6× 115 1.5× 80 1.4× 61 1.1× 20 0.4× 17 257
Adam Walter‐Croneck Poland 10 110 1.2× 130 1.7× 174 3.0× 39 0.7× 25 0.5× 32 276
Waled Bahaj United States 9 64 0.7× 53 0.7× 84 1.4× 27 0.5× 44 0.8× 36 200
Nebojša Manojlović Serbia 8 58 0.6× 215 2.8× 25 0.4× 22 0.4× 43 0.8× 25 277
Alexandra Wactor United States 5 82 0.9× 47 0.6× 54 0.9× 102 1.8× 8 0.1× 6 232

Countries citing papers authored by Weiying Gu

Since Specialization
Citations

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

Fields of papers citing papers by Weiying Gu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Weiying Gu

This figure shows the co-authorship network connecting the top 25 collaborators of Weiying Gu. A scholar is included among the top collaborators of Weiying Gu 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 Weiying Gu. Weiying Gu 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.
Zhou, Dongmei, Ling Shao, Libo Yang, et al.. (2025). A Machine Learning Model for Predicting Diabetic Nephropathy Based on TG/Cys-C Ratio and Five Clinical Indicators. Diabetes Metabolic Syndrome and Obesity. Volume 18. 955–967. 3 indexed citations
2.
3.
Yin, Lingling, Bin Lv, Jieyun Xia, et al.. (2025). The impact of antibiotic use on outcomes of relapsed/refractory multiple myeloma patients treated with CAR-T therapy. Frontiers in Immunology. 16. 1566016–1566016.
4.
Chen, Lujun, Yuan Shi, Weiying Gu, et al.. (2024). Efficacy and safety of bispecific antibodies vs. immune checkpoint blockade combination therapy in cancer: a real-world comparison. Molecular Cancer. 23(1). 77–77. 20 indexed citations
5.
Wang, Fei, Xiaonan Shao, Rong Niu, et al.. (2024). Metabolic tumour area: a novel prognostic indicator based on 18F-FDG PET/CT in patients with diffuse large B-cell lymphoma in the R-CHOP era. BMC Cancer. 24(1). 895–895. 1 indexed citations
6.
Cao, Yang, Yue Liu, Yan Liu, et al.. (2023). [Effects of Decitabine Combined with Anlotinib on Proliferation and Apoptosis of Multiple Myeloma Cells].. PubMed. 31(2). 442–447.
7.
Wang, Fei, et al.. (2023). Altered serum lipid levels are associated with prognosis of diffuse large B cell lymphoma and influenced by utility of rituximab. Annals of Hematology. 102(2). 393–402. 5 indexed citations
8.
Cao, Yang, X. Mao, Fei Wang, et al.. (2022). Novel myeloma patient-derived xenograft models unveil the potency of anlotinib to overcome bortezomib resistance. Frontiers in Oncology. 12. 894279–894279. 4 indexed citations
9.
Shen, Ziyuan, Shuo Zhang, Meng Zhang, et al.. (2022). The Addition of Ferritin Enhanced the Prognostic Value of International Prognostic Index in Diffuse Large B‐Cell Lymphoma. Frontiers in Oncology. 11. 823079–823079. 5 indexed citations
10.
Cao, Yang, Fei Wang, Naidong Zhang, et al.. (2022). Bortezomib-resistant multiple myeloma patient-derived xenograft is sensitive to anti-CD47 therapy. Leukemia Research. 122. 106949–106949. 5 indexed citations
11.
12.
Cao, Yang, Yue Liu, Huijuan Chen, et al.. (2022). Overexpression of miR-17 predicts adverse prognosis and disease recurrence for acute myeloid leukemia. International Journal of Clinical Oncology. 27(7). 1222–1232. 7 indexed citations
13.
Cao, Yang, Meng Liu, Jia Liu, et al.. (2021). Directly targeting c-Myc contributes to the anti-multiple myeloma effect of anlotinib. Cell Death and Disease. 12(4). 396–396. 16 indexed citations
14.
Shen, Ziyuan, Fei Wang, Chenlu He, et al.. (2021). The Value of Prognostic Nutritional Index (PNI) on Newly Diagnosed Diffuse Large B-Cell Lymphoma Patients: A Multicenter Retrospective Study of HHLWG Based on Propensity Score Matched Analysis. Journal of Inflammation Research. Volume 14. 5513–5522. 19 indexed citations
15.
Shen, Ziyuan, Ling Wang, Tianci Li, et al.. (2021). Development and Validation of a Novel Prognostic Nomogram for CD5-Positive Diffuse Large B-Cell Lymphoma: A Retrospective Multicenter Study in China. Frontiers in Oncology. 11. 754180–754180. 3 indexed citations
16.
Li, Feng, Weiying Gu, Xiaohua Wang, et al.. (2019). A randomized phase II, open-label and multicenter study of combination regimens of bortezomib at two doses by subcutaneous injection for newly diagnosed multiple myeloma patients. Journal of Cancer Research and Clinical Oncology. 145(9). 2343–2355. 4 indexed citations
17.
Xie, Xiaobao, Yan Lin, Yang Cao, et al.. (2018). Autologous stem cell transplantation in EBV-positive post-renal transplant refractory multiple myeloma: A case report and literature review. Oncology Letters. 15(5). 7207–7214. 1 indexed citations
18.
Zheng, Zhuojun, et al.. (2016). Prognostic Significance of MiRNA in Patients with Diffuse Large B-Cell Lymphoma: a Meta-Analysis. Cellular Physiology and Biochemistry. 39(5). 1891–1904. 16 indexed citations
19.
Cao, Yang, Guoqiang Qiu, Zhilin Wang, et al.. (2016). Decitabine enhances bortezomib treatment in RPMI 8226 multiple myeloma cells. Molecular Medicine Reports. 14(4). 3469–3475. 15 indexed citations
20.
Xie, Xiaobao, et al.. (2011). Clinical reagents of GM-CSF and IFN-α induce the generation of functional chronic myeloid leukemia dendritic cells in vitro. Cytotechnology. 64(1). 75–81. 6 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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