Fangyan Dai

1.9k total citations · 1 hit paper
25 papers, 1.4k citations indexed

About

Fangyan Dai is a scholar working on Molecular Biology, Hematology and Cell Biology. According to data from OpenAlex, Fangyan Dai has authored 25 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 5 papers in Hematology and 5 papers in Cell Biology. Recurrent topics in Fangyan Dai's work include TGF-β signaling in diseases (7 papers), Acute Myeloid Leukemia Research (4 papers) and Chronic Myeloid Leukemia Treatments (3 papers). Fangyan Dai is often cited by papers focused on TGF-β signaling in diseases (7 papers), Acute Myeloid Leukemia Research (4 papers) and Chronic Myeloid Leukemia Treatments (3 papers). Fangyan Dai collaborates with scholars based in United States, China and Taiwan. Fangyan Dai's co-authors include Kai Chen, Yi Zhou, Xia Lin, Xin‐Hua Feng, Chenbei Chang, Hua He, Long Yu, Yongjing Chen, Shouyuan Zhao and Chaoqun Wu and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Fangyan Dai

23 papers receiving 1.4k citations

Hit Papers

A LSTM-based method for stock returns prediction: A case ... 2015 2026 2018 2022 2015 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fangyan Dai United States 15 651 319 212 171 139 25 1.4k
Hye‐Jung Chung United States 17 711 1.1× 216 0.7× 37 0.2× 146 0.9× 119 0.9× 25 1.3k
Atsushi Iwasaki Japan 19 548 0.8× 260 0.8× 114 0.5× 22 0.1× 105 0.8× 76 1.9k
Miguél Vázquez Spain 20 1.0k 1.6× 35 0.1× 49 0.2× 258 1.5× 288 2.1× 65 1.9k
Stefano Moretti France 16 312 0.5× 119 0.4× 22 0.1× 50 0.3× 156 1.1× 57 839
Richard A. Dean United States 18 617 0.9× 39 0.1× 56 0.3× 54 0.3× 527 3.8× 67 1.8k
Jinbo Huang China 16 1.3k 2.0× 20 0.1× 65 0.3× 260 1.5× 599 4.3× 88 2.0k
Robert L. Perry Canada 22 1.2k 1.9× 57 0.2× 148 0.7× 6 0.0× 124 0.9× 65 2.0k
Nilamadhab Mishra India 24 1.4k 2.1× 18 0.1× 136 0.6× 19 0.1× 217 1.6× 91 2.6k
Arpita Biswas India 13 406 0.6× 77 0.2× 30 0.1× 19 0.1× 16 0.1× 33 914
Julie Zhou Canada 15 316 0.5× 194 0.6× 25 0.1× 9 0.1× 57 0.4× 67 809

Countries citing papers authored by Fangyan Dai

Since Specialization
Citations

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

Fields of papers citing papers by Fangyan Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fangyan Dai

This figure shows the co-authorship network connecting the top 25 collaborators of Fangyan Dai. A scholar is included among the top collaborators of Fangyan 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 Fangyan Dai. Fangyan Dai 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.
Lachowiez, Curtis A., Joshua F. Zeidner, D. Peters, et al.. (2024). Influence of AML Differentiation State in Risk Stratification of Frontline Therapy with Hypomethylating Agents + Venetoclax in AML. Blood. 144(Supplement 1). 62–62.
2.
Lambo, Sander, Diane L. Trinh, Rhonda E. Ries, et al.. (2023). A longitudinal single-cell atlas of treatment response in pediatric AML. Cancer Cell. 41(12). 2117–2135.e12. 31 indexed citations
3.
Pardo, Laura, Andrew P. Voigt, Todd A. Alonzo, et al.. (2019). Deciphering the Significance of CD56 Expression in Pediatric Acute Myeloid Leukemia: A Report from the Children's Oncology Group. Cytometry Part B Clinical Cytometry. 98(1). 52–56. 13 indexed citations
4.
Dai, Fangyan, Hyemin Lee, Yilei Zhang, et al.. (2017). BAP1 inhibits the ER stress gene regulatory network and modulates metabolic stress response. Proceedings of the National Academy of Sciences. 114(12). 3192–3197. 79 indexed citations
5.
Lee, Hyemin, Fangyan Dai, Li Zhuang, et al.. (2016). BAF180 regulates cellular senescence and hematopoietic stem cell homeostasis through p21. Oncotarget. 7(15). 19134–19146. 50 indexed citations
6.
Jia, Shunji, Fangyan Dai, Di Wu, et al.. (2012). Protein Phosphatase 4 Cooperates with Smads to Promote BMP Signaling in Dorsoventral Patterning of Zebrafish Embryos. Developmental Cell. 22(5). 1065–1078. 20 indexed citations
7.
Qin, Jun, San‐Pin Wu, Chad J. Creighton, et al.. (2012). COUP-TFII inhibits TGF-β-induced growth barrier to promote prostate tumorigenesis. Nature. 493(7431). 236–240. 146 indexed citations
8.
Dai, Fangyan, Tao Shen, Zhaoyong Li, Xia Lin, & Xin‐Hua Feng. (2011). PPM1A dephosphorylates RanBP3 to enable efficient nuclear export of Smad2 and Smad3. EMBO Reports. 12(11). 1175–1181. 25 indexed citations
9.
Dai, Fangyan, et al.. (2011). PPM1A dephosphorylates RanBP3 to enable efficient nuclear export of Smad2 and Smad3. EMBO Reports. 12(11). 1204–1204. 2 indexed citations
10.
Dai, Fangyan, et al.. (2010). Coupling of Dephosphorylation and Nuclear Export of Smads in TGF-β Signaling. Methods in molecular biology. 647. 125–137. 12 indexed citations
11.
Dai, Fangyan, Xia Lin, Chenbei Chang, & Xin‐Hua Feng. (2009). Nuclear Export of Smad2 and Smad3 by RanBP3 Facilitates Termination of TGF-β Signaling. Developmental Cell. 16(3). 345–357. 82 indexed citations
12.
Wrighton, Katharine H., Fangyan Dai, & Xin‐Hua Feng. (2008). A New Kid on the TGFβ Block: TAZ Controls Smad Nucleocytoplasmic Shuttling. Developmental Cell. 15(1). 8–10. 14 indexed citations
13.
Dai, Fangyan, Chenbei Chang, Xia Lin, et al.. (2007). Erbin Inhibits Transforming Growth Factor β Signaling through a Novel Smad-Interacting Domain. Molecular and Cellular Biology. 27(17). 6183–6194. 49 indexed citations
14.
Sharp, Z. Dave, Maureen G. Mancini, Cruz A. Hinojos, et al.. (2006). Estrogen-receptor-α exchange and chromatin dynamics are ligand- and domain-dependent. Journal of Cell Science. 119(19). 4101–4116. 92 indexed citations
15.
He, Hua, Yongjun Dang, Fangyan Dai, et al.. (2003). Post-translational Modifications of Three Members of the Human MAP1LC3 Family and Detection of a Novel Type of Modification for MAP1LC3B. Journal of Biological Chemistry. 278(31). 29278–29287. 239 indexed citations
16.
Dai, Fangyan, Long Yu, Hua He, et al.. (2002). Human serum and glucocorticoid-inducible kinase-like kinase (SGKL) phosphorylates glycogen syntheses kinase 3 beta (GSK-3β) at serine-9 through direct interaction. Biochemical and Biophysical Research Communications. 293(4). 1191–1196. 27 indexed citations
18.
Zhang, Pingzhao, Long Yu, Jie Gao, et al.. (2000). Cloning and Characterization of Human VPS35 and Mouse Vps35 and Mapping of VPS35 to Human Chromosome 16q13–q21. Genomics. 70(2). 253–257. 13 indexed citations
19.
Fan, Yuxin, Long Yu, Qí Zhāng, et al.. (1999). Cloning and characterization of a novel member of human β-1,4-galactosyltransferase gene family. Science in China Series C Life Sciences. 42(4). 337–345. 1 indexed citations
20.
Dai, Fangyan, Hua He, Yong Zhao, et al.. (1999). Cloning and Mapping of a Novel Human Serum/Glucocorticoid Regulated Kinase-like Gene, SGKL, to Chromosome 8q12.3–q13.1. Genomics. 62(1). 95–97. 21 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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