Hong Dai

585 total citations
21 papers, 392 citations indexed

About

Hong Dai is a scholar working on Molecular Biology, Immunology and Rheumatology. According to data from OpenAlex, Hong Dai has authored 21 papers receiving a total of 392 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 5 papers in Immunology and 4 papers in Rheumatology. Recurrent topics in Hong Dai's work include Systemic Lupus Erythematosus Research (4 papers), Digestive system and related health (3 papers) and T-cell and B-cell Immunology (3 papers). Hong Dai is often cited by papers focused on Systemic Lupus Erythematosus Research (4 papers), Digestive system and related health (3 papers) and T-cell and B-cell Immunology (3 papers). Hong Dai collaborates with scholars based in China, Taiwan and United States. Hong Dai's co-authors include George C. Tsokos, Vasileios C. Kyttaris, Fan He, Wei‐xian Chen, Tongbao Feng, Yingxin Sun, Qi Qian, Lin Cheng, Hongxing Zhou and Zhe Xu and has published in prestigious journals such as The Journal of Immunology, PLoS ONE and International Journal of Cancer.

In The Last Decade

Hong Dai

19 papers receiving 387 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Hong Dai China	12	148	148	105	94	87	21	392
Jiayi Zhang China	14	211 1.4×	113 0.8×	80 0.8×	90 1.0×	137 1.6×	36	516
Yunyun Fei China	9	71 0.5×	148 1.0×	203 1.9×	44 0.5×	77 0.9×	14	368
Ye‐Sheng Wei China	14	86 0.6×	318 2.1×	86 0.8×	128 1.4×	47 0.5×	21	464
Shanzhao Jin China	6	203 1.4×	118 0.8×	61 0.6×	93 1.0×	118 1.4×	8	466
Véronique Durand France	9	112 0.8×	73 0.5×	66 0.6×	61 0.6×	51 0.6×	18	423
Tiina M. Järvinen Finland	10	83 0.6×	158 1.1×	121 1.2×	88 0.9×	31 0.4×	14	389
Jérôme Chetritt France	10	96 0.6×	178 1.2×	72 0.7×	82 0.9×	39 0.4×	32	474
Stephanie Roberson United States	6	113 0.8×	394 2.7×	118 1.1×	263 2.8×	42 0.5×	10	626
Jing Yao Leong Singapore	10	126 0.9×	294 2.0×	44 0.4×	130 1.4×	40 0.5×	18	494
Saïda Aarrass Netherlands	7	88 0.6×	174 1.2×	138 1.3×	70 0.7×	22 0.3×	14	368

Countries citing papers authored by Hong Dai

Since Specialization

Citations

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

Fields of papers citing papers by Hong Dai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hong Dai

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Dai, Hong, Chung-Hong Lee, Zhou-shen Zhao, et al.. (2025). Leveraging large language models for the deidentification and temporal normalization of sensitive health information in electronic health records. npj Digital Medicine. 8(1). 517–517.

Zhu, Bei, et al.. (2020). Identification of prognostic significance of BIRC5 in breast cancer using integrative bioinformatics analysis. Bioscience Reports. 40(2). 37 indexed citations

Feng, Tongbao, Ping Zhang, Yingxin Sun, et al.. (2019). High throughput sequencing identifies breast cancer-secreted exosomal LncRNAs initiating pulmonary pre-metastatic niche formation. Gene. 710. 258–264. 36 indexed citations

Zhang, Pei, Hong Dai, & Lei Peng. (2019). Involvement of STAT3 Signaling in High Glucose-Induced Epithelial Mesenchymal Transition in Human Peritoneal Mesothelial Cell Line HMrSV5. Kidney & Blood Pressure Research. 44(2). 179–187. 14 indexed citations

Chen, Wei‐xian, Lin Cheng, Qi Qian, et al.. (2019). Bioinformatics analysis of potential therapeutic targets among ARHGAP genes in breast cancer. Oncology Letters. 18(6). 6017–6025. 18 indexed citations

Dai, Hong, et al.. (2019). MicroRNA-222 promotes drug resistance to doxorubicin in breast cancer via regulation of miR-222/bim pathway. Bioscience Reports. 39(7). 26 indexed citations

Zhang, Pei, Hong Dai, & Lei Peng. (2019). AGEs induce epithelial to mesenchymal transformation of human peritoneal mesothelial cells via upregulation of STAT3. Glycoconjugate Journal. 36(2). 155–163. 5 indexed citations

Cheng, Lin, Liang Shi, & Hong Dai. (2019). Bioinformatics analysis of potential prognostic biomarkers among Krüppel-like transcription Factors (KLFs) in breast cancer. Cancer Biomarkers. 26(4). 411–420. 11 indexed citations

Dai, Hong, Hongxing Zhou, Yingxin Sun, et al.. (2018). D‑dimer as a potential clinical marker for predicting metastasis and progression in cancer. Biomedical Reports. 9(5). 453–457. 41 indexed citations

10.

Dai, Hong, Fan He, George C. Tsokos, & Vasileios C. Kyttaris. (2017). IL-23 Limits the Production of IL-2 and Promotes Autoimmunity in Lupus. The Journal of Immunology. 199(3). 903–910. 87 indexed citations

11.

Dai, Enyu, Jing Wang, Xu Zhou, et al.. (2017). Accurate prediction and elucidation of drug resistance based on the robust and reproducible chemoresponse communities. International Journal of Cancer. 142(7). 1427–1439. 3 indexed citations

12.

Dai, Hong, Ankit Saxena, Anil K. Jaiswal, et al.. (2015). Syndecan‐1 identifies and controls the frequency of IL‐17‐producing naïve natural killer T (NKT17) cells in mice. European Journal of Immunology. 45(11). 3045–3051. 29 indexed citations

13.

Singh, Onkar, et al.. (2015). NTTMUNSW BioC Modules for Recognizing and Normalizing Species and Gene/Protein Mentions in Full Text Articles. 2 indexed citations

14.

Dai, Hong, et al.. (2014). A New Method of Battlefield Situation Collaborative Plotting Based on Ontology. Applied Mechanics and Materials. 513-517. 1365–1371.

15.

Zhang, Wei, et al.. (2013). Peritoneal Dialysis–Related Peritonitis with Acinetobacter Baumannii: A Review of Seven Cases. Peritoneal Dialysis International. 34(3). 317–321. 11 indexed citations

16.

Dai, Hong, et al.. (2013). Collective Instance-Level Gene Normalization on the IGN Corpus. PLoS ONE. 8(11). e79517–e79517. 11 indexed citations

17.

Tian, Xue‐Fei, et al.. (2008). [A two-year animal experimental study on the pathological effects of Helicobacter pylori on liver tissues].. PubMed. 16(2). 129–33. 4 indexed citations

18.

Li, Lianhong, Wenxian Li, Guoqing Zhang, et al.. (2008). Fas expression on peripheral blood lymphocytes in systemic lupus erythematosus: relation to the organ damage and lymphocytes apoptosis. Molecular Biology Reports. 36(8). 2047–2052. 9 indexed citations

19.

Pan, Hai‐Feng, Dong Qing Ye, Qian Wang, et al.. (2007). Clinical and laboratory profiles of systemic lupus erythematosus associated with Sjögren syndrome in China: a study of 542 patients. Clinical Rheumatology. 27(3). 339–343. 29 indexed citations

20.

Ye, Dong Qing, Hong Dai, Fen Huang, et al.. (2005). Elevated levels of serum soluble Fas are associated with organ and tissue damage in systemic lupus erythematosus among Chinese. Archives of Dermatological Research. 297(7). 329–332. 17 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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