Hailiang Ge

1.4k total citations · 1 hit paper
38 papers, 1.2k citations indexed

About

Hailiang Ge is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Hailiang Ge has authored 38 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 14 papers in Immunology and 6 papers in Oncology. Recurrent topics in Hailiang Ge's work include Immunotherapy and Immune Responses (8 papers), T-cell and B-cell Immunology (6 papers) and RNA Interference and Gene Delivery (5 papers). Hailiang Ge is often cited by papers focused on Immunotherapy and Immune Responses (8 papers), T-cell and B-cell Immunology (6 papers) and RNA Interference and Gene Delivery (5 papers). Hailiang Ge collaborates with scholars based in China, United States and Croatia. Hailiang Ge's co-authors include Frank R. Sharp, Jun Chen, Ruiqiong Ran, Guodong Cao, Yumin Luo, Aigang Lu, Steven H. Graham, Wei Pei, Shaoxiong Yu and Ying Wang and has published in prestigious journals such as Journal of Neuroscience, The Journal of Immunology and PLoS ONE.

In The Last Decade

Hailiang Ge

36 papers receiving 1.1k citations

Hit Papers

The A137R Protein of African Swine Fever Virus Inhibits T... 2022 2026 2023 2024 2022 25 50 75

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hailiang Ge China 16 569 294 216 205 180 38 1.2k
Michael R. Olin United States 25 313 0.6× 519 1.8× 294 1.4× 392 1.9× 188 1.0× 46 1.6k
Rafael Aldabe Spain 24 785 1.4× 309 1.1× 246 1.1× 241 1.2× 487 2.7× 56 1.8k
Graziella Piras United States 13 665 1.2× 227 0.8× 105 0.5× 206 1.0× 84 0.5× 16 1.2k
José Rivera Spain 16 911 1.6× 202 0.7× 194 0.9× 109 0.5× 159 0.9× 35 1.5k
Simon Yu United States 11 474 0.8× 587 2.0× 233 1.1× 104 0.5× 150 0.8× 12 1.1k
Takeshi Kameyama Japan 13 465 0.8× 484 1.6× 221 1.0× 147 0.7× 336 1.9× 30 1.2k
Amy Galliher-Beckley United States 15 486 0.9× 256 0.9× 301 1.4× 143 0.7× 53 0.3× 19 1.2k
Saı̈d Taouji France 19 590 1.0× 111 0.4× 129 0.6× 222 1.1× 298 1.7× 44 1.3k
Stephen F. Sells United States 20 1.3k 2.2× 199 0.7× 374 1.7× 84 0.4× 178 1.0× 39 1.9k
Guochun Jiang United States 23 1.1k 1.9× 321 1.1× 269 1.2× 499 2.4× 209 1.2× 42 1.8k

Countries citing papers authored by Hailiang Ge

Since Specialization
Citations

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

Fields of papers citing papers by Hailiang Ge

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hailiang Ge

This figure shows the co-authorship network connecting the top 25 collaborators of Hailiang Ge. A scholar is included among the top collaborators of Hailiang Ge 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 Hailiang Ge. Hailiang Ge 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
2.
Li, Zixin, Yu Zhou, Jin Cui, et al.. (2024). Pathogenicity comparison between porcine G9P[23] and G5P[23] RVA in piglets. Veterinary Microbiology. 301. 110359–110359.
3.
Zhang, Kehui, Hailiang Ge, Pingping Zhou, et al.. (2023). The D129L protein of African swine fever virus interferes with the binding of transcriptional coactivator p300 and IRF3 to prevent beta interferon induction. Journal of Virology. 97(10). e0082423–e0082423. 8 indexed citations
4.
Song, Feifei, Qi Chen, Wei Rao, et al.. (2019). OVA66 promotes tumour angiogenesis and progression through enhancing autocrine VEGF-VEGFR2 signalling. EBioMedicine. 41. 156–166. 26 indexed citations
5.
Ji, Ping, Yong Zhang, Shujun Wang, et al.. (2016). CD44hiCD24lo mammosphere-forming cells from primary breast cancer display resistance to multiple chemotherapeutic drugs. Oncology Reports. 35(6). 3293–3302. 28 indexed citations
6.
Ge, Hailiang, et al.. (2015). Enhanced performance of photonic crystal GaN light-emitting diodes with graphene transparent electrodes. Nanoscale Research Letters. 10(1). 103–103. 7 indexed citations
7.
Rao, Wei, Haowen Li, Feifei Song, et al.. (2014). OVA66 increases cell growth, invasion and survival via regulation of IGF-1R–MAPK signaling in human cancer cells. Carcinogenesis. 35(7). 1573–1581. 16 indexed citations
8.
Xiang, Ming-Jie, Jinyan Liu, Peihua Ni, et al.. (2013). Erg11mutations associated with azole resistance in clinical isolates ofCandida albicans. FEMS Yeast Research. 13(4). 386–393. 146 indexed citations
9.
Wang, Qingfei, Shau-Hsuan Li, Hai Wang, et al.. (2012). Concomitant Targeting of Tumor Cells and Induction of T-cell Response Synergizes to Effectively Inhibit Trastuzumab-Resistant Breast Cancer. Cancer Research. 72(17). 4417–4428. 33 indexed citations
10.
Wang, Ming, Yuhua Qiu, Xuelei Wang, et al.. (2010). Role of HLA‐G and NCR in protection of umbilical cord blood haematopoietic stem cells from NK cell mediated cytotoxicity. Journal of Cellular and Molecular Medicine. 15(10). 2040–2045. 3 indexed citations
11.
Zhang, Chunhui, et al.. (2010). Elevated IGFIR expression regulating VEGF and VEGF-C predicts lymph node metastasis in human colorectal cancer. BMC Cancer. 10(1). 184–184. 37 indexed citations
12.
Wu, Xia, et al.. (2009). The immunologic aspects in advanced ovarian cancer patients treated with paclitaxel and carboplatin chemotherapy. Cancer Immunology Immunotherapy. 59(2). 279–291. 65 indexed citations
13.
Wang, Xuefeng, Peipei Jin, Tong Zhou, et al.. (2009). MR Molecular Imaging of Thrombus: Development and Application of a Gd-based Novel Contrast Agent Targeting to P-selectin. Clinical and Applied Thrombosis/Hemostasis. 16(2). 177–183. 13 indexed citations
14.
He, Shan, Qi Cao, Hiroyuki Yoneyama, et al.. (2008). MIP-3α and MIP-1α rapidly mobilize dendritic cell precursors into the peripheral blood. Journal of Leukocyte Biology. 84(6). 1549–1556. 18 indexed citations
15.
He, Shan, Qi Cao, Yuhua Qiu, et al.. (2008). A New Approach to the Blocking of Alloreactive T Cell-Mediated Graft-versus-Host Disease by In Vivo Administration of Anti-CXCR3 Neutralizing Antibody. The Journal of Immunology. 181(11). 7581–7592. 42 indexed citations
16.
Wang, Qingfei, Meixing Li, Ying Wang, et al.. (2008). RNA interference targeting CML66, a novel tumor antigen, inhibits proliferation, invasion and metastasis of HeLa cells. Cancer Letters. 269(1). 127–138. 31 indexed citations
17.
Xie, Guohua, Shujun Wang, Ying Wang, et al.. (2008). Fas Ligand gene transfer enhances the survival of tissue-engineered chondrocyte allografts in mini-pigs. Transplant Immunology. 19(2). 145–151. 5 indexed citations
19.
Ge, Hailiang, et al.. (2007). OBF-1 is essential for the generation of antibody-secreting cells and the development of autoimmunity in MRL-lpr mice. Journal of Autoimmunity. 29(2-3). 87–96. 4 indexed citations
20.
Shu, Jin, Ying Wang, Shujun Wang, et al.. (2005). [Identification of a novel HLA-A2-restrictive CTL epitope of an ovary cancer-associated antigen OVA66].. PubMed. 21(2). 233–6, 242. 4 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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