Jingxia Xu

1.6k total citations
22 papers, 873 citations indexed

About

Jingxia Xu is a scholar working on Molecular Biology, Immunology and Biomedical Engineering. According to data from OpenAlex, Jingxia Xu has authored 22 papers receiving a total of 873 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 3 papers in Immunology and 3 papers in Biomedical Engineering. Recurrent topics in Jingxia Xu's work include Bioinformatics and Genomic Networks (5 papers), Gene expression and cancer classification (5 papers) and Developmental Biology and Gene Regulation (4 papers). Jingxia Xu is often cited by papers focused on Bioinformatics and Genomic Networks (5 papers), Gene expression and cancer classification (5 papers) and Developmental Biology and Gene Regulation (4 papers). Jingxia Xu collaborates with scholars based in United States, China and Russia. Jingxia Xu's co-authors include Thomas Gridley, Amy E. Kiernan, Nasser Chegini, Li Ding, Xiaoping Luo, Joel E. Richardson, Jacqueline H. Finger, Constance M. Smith, James A. Kadin and Terry F. Hayamizu and has published in prestigious journals such as Nucleic Acids Research, ACS Nano and The Journal of Clinical Endocrinology & Metabolism.

In The Last Decade

Jingxia Xu

20 papers receiving 862 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jingxia Xu United States 13 489 195 158 135 100 22 873
Philippe Vago France 20 422 0.9× 262 1.3× 72 0.5× 15 0.1× 297 3.0× 89 1.2k
Ruth Grümmer Germany 26 1.0k 2.1× 211 1.1× 743 4.7× 503 3.7× 237 2.4× 52 2.0k
Yunbo Qiao China 21 804 1.6× 134 0.7× 19 0.1× 16 0.1× 144 1.4× 49 1.1k
Brice Marcet France 16 653 1.3× 67 0.3× 30 0.2× 14 0.1× 213 2.1× 24 1.2k
Gerard W. Dougherty Germany 17 419 0.9× 53 0.3× 49 0.3× 8 0.1× 437 4.4× 25 1.0k
Igor Kostetskii United States 19 1.3k 2.7× 21 0.1× 202 1.3× 18 0.1× 554 5.5× 25 1.9k
Michio Fujiwara Japan 14 724 1.5× 25 0.1× 61 0.4× 21 0.2× 270 2.7× 33 1.1k
Debora Bogani United Kingdom 22 1.2k 2.5× 58 0.3× 132 0.8× 6 0.0× 615 6.2× 29 1.6k
Christian W. Ehrenfels United States 9 392 0.8× 11 0.1× 153 1.0× 66 0.5× 166 1.7× 9 731
Raman M Das United Kingdom 16 661 1.4× 14 0.1× 80 0.5× 22 0.2× 272 2.7× 34 1.0k

Countries citing papers authored by Jingxia Xu

Since Specialization
Citations

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

Fields of papers citing papers by Jingxia Xu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jingxia Xu

This figure shows the co-authorship network connecting the top 25 collaborators of Jingxia Xu. A scholar is included among the top collaborators of Jingxia Xu 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 Jingxia Xu. Jingxia Xu 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.
Smith, Constance M., Terry F. Hayamizu, Jacqueline H. Finger, et al.. (2025). The mouse Gene Expression Database (GXD): 2026 update. Nucleic Acids Research. 54(D1). D1190–D1196.
2.
Xu, Xiaoman, Yunmei Song, Mingli Li, et al.. (2025). Biomimetic tumor cell membrane-camouflaged nanomicelles for synergistic chemo-immunotherapy of Triple-negative breast cancer. Materials Today Bio. 33. 102012–102012.
3.
Zhang, Ziyao, Jingxia Xu, Fangming Zhang, et al.. (2025). Enhancing Necroptotic Chemo-Immunotherapy of Breast Cancer by Reactive Oxygen Species Generating Lipid Nanoparticles. ACS Applied Nano Materials. 8(28). 14470–14480. 1 indexed citations
4.
Xu, Jingxia, Xiaoman Xu, Huiwen Zhang, et al.. (2024). Tumor-associated inflammation: The role and research progress in tumor therapy. Journal of Drug Delivery Science and Technology. 102. 106376–106376. 1 indexed citations
5.
Luo, Shuang, Wei He, Zhanhui Feng, et al.. (2023). The Role of Signaling Pathways in Pancreatic Cancer Targeted Therapy. American Journal of Clinical Oncology. 46(3). 121–128. 3 indexed citations
6.
Yan, Fanyong, et al.. (2022). Facile preparation of sulfur quantum dots involving β-cyclodextrin for ratiometric fluorescence/scattered light detection of acridine orange. Materials Research Bulletin. 150. 111765–111765. 17 indexed citations
7.
Baldarelli, Richard M., Constance M. Smith, Jacqueline H. Finger, et al.. (2020). The mouse Gene Expression Database (GXD): 2021 update. Nucleic Acids Research. 49(D1). D924–D931. 75 indexed citations
8.
Finger, Jacqueline H., Constance M. Smith, Terry F. Hayamizu, et al.. (2015). The mouse gene expression database: New features and how to use them effectively. genesis. 53(8). 510–522. 11 indexed citations
9.
Smith, Constance M., Jacqueline H. Finger, Terry F. Hayamizu, et al.. (2015). GXD: a community resource of mouse Gene Expression Data. Mammalian Genome. 26(7-8). 314–324. 18 indexed citations
10.
Wu, Yun, et al.. (2014). Benzocyclobutene (BCB) Polymer as Amphibious Buffer Layer for Graphene Field-Effect Transistor. Journal of Nanoscience and Nanotechnology. 15(8). 5706–5710. 2 indexed citations
11.
Xu, Jingxia & Thomas Gridley. (2013). Notch2 is required in somatic cells for breakdown of ovarian germ-cell nests and formation of primordial follicles. BMC Biology. 11(1). 13–13. 99 indexed citations
12.
Smith, Constance M., Jacqueline H. Finger, Terry F. Hayamizu, et al.. (2013). The mouse Gene Expression Database (GXD): 2014 update. Nucleic Acids Research. 42(D1). D818–D824. 65 indexed citations
13.
Xu, Jingxia & Thomas Gridley. (2012). Notch Signaling during Oogenesis inDrosophila melanogaster. PubMed. 2012. 1–10. 24 indexed citations
14.
Krebs, Luke T., Cara K. Bradley, Christine R. Norton, et al.. (2011). The Notch‐regulated ankyrin repeat protein is required for proper anterior–posterior somite patterning in mice. genesis. 50(4). 366–374. 6 indexed citations
15.
Xu, Jingxia, Luke T. Krebs, & Thomas Gridley. (2010). Generation of mice with a conditional null allele of the Jagged2 gene. genesis. 48(6). 390–393. 24 indexed citations
16.
Kiernan, Amy E., Jingxia Xu, & Thomas Gridley. (2006). The Notch Ligand JAG1 Is Required for Sensory Progenitor Development in the Mammalian Inner Ear. PLoS Genetics. 2(1). e4–e4. 248 indexed citations
17.
Kiernan, Amy E., Jingxia Xu, & Thomas Gridley. (2005). The Notch ligand JAG1 is required for sensory progenitor development in the mammalian inner ear. PLoS Genetics. preprint(2005). e4–e4. 14 indexed citations
18.
Luo, Xiaoping, Li Ding, Jingxia Xu, & Nasser Chegini. (2004). Gene Expression Profiling of Leiomyoma and Myometrial Smooth Muscle Cells in Response to Transforming Growth Factor-β. Endocrinology. 146(3). 1097–1118. 91 indexed citations
19.
Ding, Li, et al.. (2004). Leiomyoma and Myometrial Gene Expression Profiles and Their Responses to Gonadotropin-Releasing Hormone Analog Therapy. Endocrinology. 146(3). 1074–1096. 36 indexed citations
20.

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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