Long H. Dang

4.2k total citations
85 papers, 3.2k citations indexed

About

Long H. Dang is a scholar working on Oncology, Molecular Biology and Cancer Research. According to data from OpenAlex, Long H. Dang has authored 85 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Oncology, 27 papers in Molecular Biology and 24 papers in Cancer Research. Recurrent topics in Long H. Dang's work include Cancer, Hypoxia, and Metabolism (17 papers), Peptidase Inhibition and Analysis (11 papers) and Cancer Research and Treatments (10 papers). Long H. Dang is often cited by papers focused on Cancer, Hypoxia, and Metabolism (17 papers), Peptidase Inhibition and Analysis (11 papers) and Cancer Research and Treatments (10 papers). Long H. Dang collaborates with scholars based in United States, Japan and China. Long H. Dang's co-authors include Duyen T. Dang, Bert Vogelstein, Kenneth W. Kinzler, Chetan Bettegowda, David L. Huso, Vincent W. Yang, Sang Y. Chun, Nicola J. Mabjeesh, Ian Cheong and Nam H. Dang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Long H. Dang

83 papers receiving 3.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Long H. Dang United States 27 1.5k 890 668 663 647 85 3.2k
Ian Cheong United States 20 1.8k 1.2× 683 0.8× 718 1.1× 660 1.0× 432 0.7× 42 3.2k
Jan Theys Netherlands 36 1.3k 0.9× 1.0k 1.1× 679 1.0× 766 1.2× 656 1.0× 98 3.1k
Enrico P. Spugnini Italy 33 1.5k 1.0× 871 1.0× 411 0.6× 714 1.1× 245 0.4× 110 3.3k
Peter W. Szlosarek United Kingdom 27 1.3k 0.8× 999 1.1× 673 1.0× 1.1k 1.7× 477 0.7× 65 3.1k
Michael Linnebacher Germany 31 1.4k 1.0× 335 0.4× 1.6k 2.5× 801 1.2× 261 0.4× 153 3.8k
Larry E. Dillehay United States 25 3.1k 2.1× 453 0.5× 1.8k 2.6× 1.9k 2.9× 370 0.6× 51 4.7k
Shigeru Sakiyama Japan 34 2.4k 1.6× 329 0.4× 935 1.4× 709 1.1× 646 1.0× 124 3.8k
Gennadi V. Glinsky United States 32 2.8k 1.8× 131 0.1× 1.0k 1.6× 825 1.2× 303 0.5× 75 4.1k
Maurizio Fanciulli Italy 32 2.8k 1.9× 272 0.3× 1.1k 1.7× 821 1.2× 338 0.5× 115 4.0k
Gloria H. Heppner United States 29 1.5k 1.0× 278 0.3× 1.6k 2.5× 1.2k 1.8× 380 0.6× 69 3.7k

Countries citing papers authored by Long H. Dang

Since Specialization
Citations

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

Fields of papers citing papers by Long H. Dang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Long H. Dang. 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 Long H. Dang. The network helps show where Long H. Dang may publish in the future.

Co-authorship network of co-authors of Long H. Dang

This figure shows the co-authorship network connecting the top 25 collaborators of Long H. Dang. A scholar is included among the top collaborators of Long H. Dang 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 Long H. Dang. Long H. Dang 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.
Chow, Kenneth, An Dang, Long H. Dang, et al.. (2023). Barriers to Lactulose Adherence in Patients with Cirrhosis and Hepatic Encephalopathy. Digestive Diseases and Sciences. 68(6). 2389–2397. 3 indexed citations
2.
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Shah, Chintan, Young‐Rock Hong, Rohit Bishnoi, et al.. (2020). Impact of DPP4 Inhibitors in Survival of Patients With Prostate, Pancreas, and Breast Cancer. Frontiers in Oncology. 10. 405–405. 34 indexed citations
4.
Yuan, Cai, et al.. (2020). Cancer Treatment Response to Checkpoint Inhibitors Is Associated with Cytomegalovirus Infection. Cureus. 12(1). e6670–e6670. 3 indexed citations
5.
Bajwa, Ravneet, Shahla Bari, William Paul Skelton, et al.. (2018). Stereotactic body radiation therapy for the treatment of oligoprogression on androgen receptor targeted therapy in castration-resistant prostate cancer. Oxford Medical Case Reports. 2018(1). omx078–omx078. 9 indexed citations
6.
Gunasekera, Sarath P., et al.. (2016). Caldoramide, a Modified Pentapeptide from the Marine Cyanobacterium Caldora penicillata. Journal of Natural Products. 79(7). 1867–1871. 26 indexed citations
7.
Ratnayake, Ranjala, Pamela A. Havre, Nam H. Dang, et al.. (2016). Multidimensional Screening Platform for Simultaneously Targeting Oncogenic KRAS and Hypoxia-Inducible Factors Pathways in Colorectal Cancer. ACS Chemical Biology. 11(5). 1322–1331. 27 indexed citations
8.
Wray, Justin, Roi Dagan, Anamaria R. Yeung, et al.. (2016). Stereotactic body radiation therapy for oligoprogression of metastatic disease from gastrointestinal cancers: A novel approach to extend chemotherapy efficacy. Oncology Letters. 13(3). 1087–1094. 12 indexed citations
9.
Golan, Maya, Sharon Amir, Duyen T. Dang, et al.. (2012). HIF1AC1772T polymorphism leads to HIF-1α mRNA overexpression in prostate cancer patients. Cancer Biology & Therapy. 13(9). 720–726. 21 indexed citations
10.
Liu, Yanxia, Steven N. Hochwald, Emina H. Huang, et al.. (2011). Can we develop effective combination antiangiogenic therapy for patients with hepatocellular carcinoma?. Oncology Reviews. 5(3). 177–184. 9 indexed citations
11.
Chun, Sang Y., et al.. (2010). Combination therapy targeting cancer metabolism. Medical Hypotheses. 76(2). 169–172. 16 indexed citations
12.
Chun, Sang Y., Craig Johnson, Joseph Washburn, et al.. (2010). Oncogenic KRAS modulates mitochondrial metabolism in human colon cancer cells by inducing HIF-1α and HIF-2α target genes. Molecular Cancer. 9(1). 293–293. 94 indexed citations
13.
Burkitt, Kyunghee, Sang Y. Chun, Duyen T. Dang, & Long H. Dang. (2009). Targeting both HIF-1 and HIF-2 in human colon cancer cells improves tumor response to sunitinib treatment. Molecular Cancer Therapeutics. 8(5). 1148–1156. 54 indexed citations
14.
Dang, Duyen T., Sang Y. Chun, Kyunghee Burkitt, et al.. (2008). Hypoxia-Inducible Factor-1 Target Genes as Indicators of Tumor Vessel Response to Vascular Endothelial Growth Factor Inhibition. Cancer Research. 68(6). 1872–1880. 62 indexed citations
15.
Dang, Duyen T., et al.. (2008). HIF-αs promote mitochondrial cardiolipin synthesis and respiration efficiency. Cancer Research. 68. 4109–4109. 1 indexed citations
16.
Dang, Duyen T., Xinming Chen, Jing Feng, et al.. (2003). Overexpression of Krüppel-like factor 4 in the human colon cancer cell line RKO leads to reduced tumorigenecity. Oncogene. 22(22). 3424–3430. 143 indexed citations
17.
Chan, Timothy A., Zhenghe Wang, Long H. Dang, Bert Vogelstein, & Kenneth W. Kinzler. (2002). Targeted inactivation of CTNNB1 reveals unexpected effects of β-catenin mutation. Proceedings of the National Academy of Sciences. 99(12). 8265–8270. 108 indexed citations
18.
Dang, Long H., Chetan Bettegowda, David L. Huso, Kenneth W. Kinzler, & Bert Vogelstein. (2001). Combination bacteriolytic therapy for the treatment of experimental tumors. Proceedings of the National Academy of Sciences. 98(26). 15155–15160. 446 indexed citations
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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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