Yaguang Xi

6.2k total citations
78 papers, 4.9k citations indexed

About

Yaguang Xi is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Yaguang Xi has authored 78 papers receiving a total of 4.9k indexed citations (citations by other indexed papers that have themselves been cited), including 59 papers in Molecular Biology, 41 papers in Cancer Research and 18 papers in Oncology. Recurrent topics in Yaguang Xi's work include MicroRNA in disease regulation (33 papers), Cancer-related molecular mechanisms research (24 papers) and Circular RNAs in diseases (17 papers). Yaguang Xi is often cited by papers focused on MicroRNA in disease regulation (33 papers), Cancer-related molecular mechanisms research (24 papers) and Circular RNAs in diseases (17 papers). Yaguang Xi collaborates with scholars based in United States, China and Norway. Yaguang Xi's co-authors include Jingfang Ju, Øystein Fodstad, Kenji Kudo, Elaine Gavin, Go Nakajima, Adam I. Riker, Rebecca A. Fillmore, A. Formentini, Gary A. Piazza and Zhengming Wang and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Journal of Clinical Oncology.

In The Last Decade

Yaguang Xi

76 papers receiving 4.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yaguang Xi United States 34 3.8k 3.0k 808 313 308 78 4.9k
Florian A. Karreth United States 25 4.7k 1.2× 2.9k 1.0× 879 1.1× 343 1.1× 372 1.2× 53 5.9k
Elena Piskounova United States 13 2.9k 0.7× 1.7k 0.6× 895 1.1× 261 0.8× 328 1.1× 22 4.1k
Peixin Dong Japan 35 2.4k 0.6× 1.8k 0.6× 942 1.2× 260 0.8× 366 1.2× 72 3.4k
Yongqian Shu China 34 3.5k 0.9× 3.2k 1.1× 442 0.5× 310 1.0× 268 0.9× 75 4.2k
Naohiro Nishida Japan 30 2.6k 0.7× 2.2k 0.8× 776 1.0× 307 1.0× 147 0.5× 72 3.6k
John G. Clohessy United States 28 2.9k 0.8× 1.4k 0.5× 756 0.9× 371 1.2× 411 1.3× 50 3.7k
Zhao-Lei Zeng China 39 3.5k 0.9× 2.6k 0.9× 1.1k 1.3× 611 2.0× 407 1.3× 90 4.9k
Guoguang Ying China 36 4.0k 1.1× 2.7k 0.9× 759 0.9× 692 2.2× 590 1.9× 88 5.1k
Jordan M. Cummins United States 17 3.1k 0.8× 1.7k 0.6× 902 1.1× 285 0.9× 272 0.9× 23 4.1k
Scott Valastyan United States 8 2.8k 0.7× 2.1k 0.7× 1.5k 1.9× 451 1.4× 403 1.3× 12 4.5k

Countries citing papers authored by Yaguang Xi

Since Specialization
Citations

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

Fields of papers citing papers by Yaguang Xi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yaguang Xi

This figure shows the co-authorship network connecting the top 25 collaborators of Yaguang Xi. A scholar is included among the top collaborators of Yaguang Xi 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 Yaguang Xi. Yaguang Xi 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.
2.
Yi, Bin, Robert M. Jones, Eugene F. Douglass, et al.. (2024). Computationally Driven Discovery of a BCR-ABL1 Kinase Inhibitor with Activity in Multidrug-Resistant Chronic Myeloid Leukemia. Journal of Medicinal Chemistry. 67(19). 17820–17832. 2 indexed citations
3.
Yi, Bin, Hao Cheng, Dorota Wyczechowska, et al.. (2021). Sulindac Modulates the Response of Proficient MMR Colorectal Cancer to Anti–PD-L1 Immunotherapy. Molecular Cancer Therapeutics. 20(7). 1295–1304. 8 indexed citations
4.
Zhao, Hongyou, et al.. (2020). Cyclin G2, a novel target of sulindac to inhibit cell cycle progression in colorectal cancer. Genes & Diseases. 8(3). 320–330. 8 indexed citations
5.
Yi, Bin, Hong Chang, Ruixia Ma, et al.. (2016). Inhibition of breast cancer cell motility with a non-cyclooxygenase inhibitory derivative of sulindac by suppressing TGFβ/miR-21 signaling. Oncotarget. 7(7). 7979–7992. 15 indexed citations
6.
Su, Liya, et al.. (2015). Anticancer bioactive peptides suppress human colorectal tumor cell growth and induce apoptosis via modulating the PARP-p53-Mcl-1 signaling pathway. Acta Pharmacologica Sinica. 36(12). 1514–1519. 33 indexed citations
7.
Chen, Feifei, Meng Wang, Jin Bai, et al.. (2014). Role of RUNX3 in Suppressing Metastasis and Angiogenesis of Human Prostate Cancer. PLoS ONE. 9(1). e86917–e86917. 39 indexed citations
8.
Li, Dongsheng, et al.. (2014). MicroRNAs and anticancer drugs. Acta Biochimica et Biophysica Sinica. 46(3). 233–239. 16 indexed citations
9.
Arora, Ritu, Bernard D. Gary, Steven McClellan, et al.. (2013). Abstract 5571: Antitumor activity of a novel natural therapeutic agent against triple negative breast cancer.. Cancer Research. 73(8_Supplement). 5571–5571. 1 indexed citations
10.
Wang, Bin & Yaguang Xi. (2013). Challenges for MicroRNA Microarray Data Analysis. PubMed. 2(2). 34–50. 37 indexed citations
11.
Tinsley, Heather N., William E. Grizzle, Alireza Abadi, et al.. (2012). New NSAID Targets and Derivatives for Colorectal Cancer Chemoprevention. Recent results in cancer research. 191. 105–120. 30 indexed citations
12.
Guldvik, Ingrid Jenny, Rahul Palchaudhuri, Yaguang Xi, et al.. (2011). Triphenylmethyl Derivatives Enhances the Anticancer Effect of Immunotoxins. Journal of Immunotherapy. 34(5). 438–447. 14 indexed citations
13.
Li, Zheng‐Xiang, et al.. (2011). MicroRNA provides insight into understanding esophageal cancer. Thoracic Cancer. 2(4). 134–142. 8 indexed citations
14.
Zhou, Ming, Zixing Liu, Yuhua Zhao, et al.. (2010). MicroRNA-125b Confers the Resistance of Breast Cancer Cells to Paclitaxel through Suppression of Pro-apoptotic Bcl-2 Antagonist Killer 1 (Bak1) Expression. Journal of Biological Chemistry. 285(28). 21496–21507. 347 indexed citations
15.
Kudo, Kenji, Yaguang Xi, Yuan Wang, et al.. (2010). Translational control analysis by translationally active RNA capture/microarray analysis (TrIP–Chip). Nucleic Acids Research. 38(9). e104–e104. 19 indexed citations
16.
Bruheim, Skjalg, Yaguang Xi, Jingfang Ju, & Øystein Fodstad. (2009). Gene Expression Profiles Classify Human Osteosarcoma Xenografts According to Sensitivity to Doxorubicin, Cisplatin, and Ifosfamide. Clinical Cancer Research. 15(23). 7161–7169. 33 indexed citations
17.
Shevde, Lalita A., Brandon J. Metge, Aparna Mitra, et al.. (2009). Spheroid‐forming subpopulation of breast cancer cells demonstrates vasculogenic mimicry via hsa‐miR‐299–5p regulated de novo expression of osteopontin. Journal of Cellular and Molecular Medicine. 14(6b). 1693–1706. 54 indexed citations
18.
Song, Bo, Yuan Wang, Kenji Kudo, et al.. (2008). miR-192 Regulates Dihydrofolate Reductase and Cellular Proliferation through the p53-microRNA Circuit. Clinical Cancer Research. 14(24). 8080–8086. 127 indexed citations
19.
Xi, Yaguang, A. Formentini, Minchen Chien, et al.. (2006). Prognostic Values of microRNAs in Colorectal Cancer.. PubMed. 2. 113–121. 233 indexed citations
20.
Xi, Yaguang. (2004). p53 polymorphism and p21WAF1/CIP1 haplotype in the intestinal gastric cancer and the precancerous lesions. Carcinogenesis. 25(11). 2201–2206. 41 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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