Junchao Dong

981 total citations
24 papers, 652 citations indexed

About

Junchao Dong is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Junchao Dong has authored 24 papers receiving a total of 652 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 9 papers in Immunology and 4 papers in Oncology. Recurrent topics in Junchao Dong's work include DNA Repair Mechanisms (9 papers), CRISPR and Genetic Engineering (8 papers) and T-cell and B-cell Immunology (8 papers). Junchao Dong is often cited by papers focused on DNA Repair Mechanisms (9 papers), CRISPR and Genetic Engineering (8 papers) and T-cell and B-cell Immunology (8 papers). Junchao Dong collaborates with scholars based in China, United States and Malaysia. Junchao Dong's co-authors include Frederick W. Alt, Rohit A. Panchakshari, Jiazhi Hu, Robin M. Meyers, Richard L. Frock, Huibing Zhang, Sang Eun Lee, Jef D. Boeke, Fuyang Li and Xuewen Pan and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Junchao Dong

23 papers receiving 637 citations

Peers

Junchao Dong

IMMUN

ONCOL

Sumana Sharma United Kingdom

Donát Alpár Hungary

Matthew G. Jones United States

Dimitra Alexopoulou Germany

Brigit Greystoke United Kingdom

Hoa Thi Tran Vietnam

Wenbao Yu United States

Shane Lofgren United States

Henrik Sahlin Pettersen Norway

Nathan Martin United States

Sumana Sharma United Kingdom

Junchao Dong

460 ×0.9

102 ×0.6

IMMUN

83 ×0.9

ONCOL

37 ×0.8

21 ×0.5

Citations per year, relative to Junchao Dong Junchao Dong (= 1×) peers Sumana Sharma

Countries citing papers authored by Junchao Dong

Since Specialization

Citations

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

Fields of papers citing papers by Junchao Dong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junchao Dong

This figure shows the co-authorship network connecting the top 25 collaborators of Junchao Dong. A scholar is included among the top collaborators of Junchao Dong 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 Junchao Dong. Junchao Dong 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

Zhao, Bo, Zhigang Xia, Yao Guo, et al.. (2024). USP7 promotes IgA class switching through stabilizing RUNX3 for germline transcription activation. Cell Reports. 43(5). 114194–114194. 2 indexed citations

Du, Likun, Valentyn Oksenych, Hui Wan, et al.. (2024). Orientation Regulation of Class-switch Recombination in Human B Cells. The Journal of Immunology. 213(8). 1093–1104. 2 indexed citations

Dong, Junchao, et al.. (2023). Integrating Multiple Linear Regression and Infectious Disease Models for Predicting Information Dissemination in Social Networks. 7(2). 20–27.

Zhao, Bo, et al.. (2022). Alternative end-joining in BCR gene rearrangements and translocations. Acta Biochimica et Biophysica Sinica. 54(6). 782–795. 2 indexed citations

Dong, Junchao, et al.. (2022). Alterations in cellular metabolisms after TKI therapy for Philadelphia chromosome-positive leukemia in children: A review. Frontiers in Oncology. 12. 1072806–1072806. 6 indexed citations

Xu, Jiejie, et al.. (2021). The 3′-flap endonuclease XPF-ERCC1 promotes alternative end joining and chromosomal translocation during B cell class switching. Cell Reports. 36(13). 109756–109756. 11 indexed citations

Xu, Jiejie, Xiaoli Xi, Mingyu Gu, et al.. (2021). Multiple DSB Resection Activities Redundantly Promote Alternative End Joining-Mediated Class Switch Recombination. Frontiers in Cell and Developmental Biology. 9. 767624–767624. 2 indexed citations

Zhang, Huibing & Junchao Dong. (2020). Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms. EURASIP Journal on Wireless Communications and Networking. 2020(1). 8 indexed citations

Chen, Jingjing, Ying Zhang, Min Huang, et al.. (2020). REV7 is required for processing AID initiated DNA lesions in activated B cells. Nature Communications. 11(1). 2812–2812. 8 indexed citations

10.

Bao, Qing, Chen Cao, Lin Sun, et al.. (2020). Presence of Tertiary Lymphoid Organ in Nasal Inverted Papilloma Is Correlated with Eosinophil Infiltration and Local Immunoglobulin Production. International Archives of Allergy and Immunology. 182(4). 350–359. 6 indexed citations

11.

Zhang, Huibing & Junchao Dong. (2020). Prediction of Repeat Customers on E-Commerce Platform Based on Blockchain. Wireless Communications and Mobile Computing. 2020. 1–15. 7 indexed citations

12.

Dong, Junchao, et al.. (2020). A Commodity Review Sentiment Analysis Based on BERT-CNN Model. 143–147. 33 indexed citations

13.

He, Juan, Qiong Yang, Qiang Xiao, et al.. (2019). IRF-7 Is a Critical Regulator of Type 2 Innate Lymphoid Cells in Allergic Airway Inflammation. Cell Reports. 29(9). 2718–2730.e6. 31 indexed citations

14.

Panchakshari, Rohit A., Xuefei Zhang, Vipul Kumar, et al.. (2018). DNA double-strand break response factors influence end-joining features of IgH class switch and general translocation junctions. Proceedings of the National Academy of Sciences. 115(4). 762–767. 39 indexed citations

15.

Nguyen, Hai V., et al.. (2017). Histone methyltransferase MMSET promotes AID-mediated DNA breaks at the donor switch region during class switch recombination. Proceedings of the National Academy of Sciences. 114(49). E10560–E10567. 12 indexed citations

16.

Hu, Jiazhi, Robin M. Meyers, Junchao Dong, et al.. (2016). Detecting DNA double-stranded breaks in mammalian genomes by linear amplification–mediated high-throughput genome-wide translocation sequencing. Nature Protocols. 11(5). 853–871. 187 indexed citations

17.

Dong, Junchao, Rohit A. Panchakshari, Tingting Zhang, et al.. (2015). Orientation-specific joining of AID-initiated DNA breaks promotes antibody class switching. Nature. 525(7567). 134–139. 80 indexed citations

18.

Gostissa, Monica, Bjoern Schwer, Amelia Chang, et al.. (2014). IgH class switching exploits a general property of two DNA breaks to be joined in cis over long chromosomal distances. Proceedings of the National Academy of Sciences. 111(7). 2644–2649. 26 indexed citations

19.

Sugawara, Neal, Junchao Dong, Rachel Toth, et al.. (2010). Mec1/Tel1-dependent phosphorylation of Slx4 stimulates Rad1–Rad10-dependent cleavage of non-homologous DNA tails. DNA repair. 9(6). 718–726. 45 indexed citations

20.

Li, Fuyang, et al.. (2008). Microarray-Based Genetic Screen Defines SAW1, a Gene Required for Rad1/Rad10-Dependent Processing of Recombination Intermediates. Molecular Cell. 30(3). 325–335. 75 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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