Fangyan Dai
Impact in
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- Stock Market Forecasting Methods
- Forecasting Techniques and Applications
Papers in
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- TGF-β signaling in diseases 7
- Nuclear Structure and Function 3
- Glycosylation and Glycoproteins Research 2
- Ubiquitin and proteasome pathways 2
- Single-cell and spatial transcriptomics 2
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- Acute Myeloid Leukemia Research 4
- Chronic Myeloid Leukemia Treatments 3
- Co-authors
- Kai Chen (1 shared paper)Yi Zhou (1 shared paper)Xia Lin (7 shared papers)Xin‐Hua Feng (8 shared papers)Chenbei Chang (2 shared papers)Hua He (3 shared papers)Long Yu (5 shared papers)Shouyuan Zhao (3 shared papers)
- Journals
- Developmental Cell (3 papers)Blood (3 papers)Journal of Cell Science (2 papers)Genomics (2 papers)EMBO Reports (2 papers)
- Partner nations
- United StatesChinaTaiwan
In The Last Decade
Fangyan Dai
23 papers receiving 1.4k citations
Fangyan Dai's Hit Papers
Peers
Comparison fields: 5 of 136
- Management Science and Operations Research 326
- Cancer Research 126
- Molecular Biology 608
- Finance 89
- Cell Biology 134
Countries citing papers authored by Fangyan Dai
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
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-authors
The 25 scholars most cited alongside Fangyan Dai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | A LSTM-based method for stock returns prediction: A case study of China stock market Hit paper breakdown → | 2015 | 428 |
| 2 | 2003 | 240 | |
| 3 | 2012 | 148 | |
| 4 | 2006 | 93 | |
| 5 | 2009 | 83 | |
| 6 | 2017 | 80 | |
| 7 | 2007 | 52 | |
| 8 | 2016 | 51 | |
| 9 | 2023 | 36 | |
| 10 | 2008 | 30 | |
| 11 | 2002 | 27 | |
| 12 | 2011 | 26 | |
| 13 | 1999 | 21 | |
| 14 | 2012 | 20 | |
| 15 | 2019 | 14 | |
| 16 | 2008 | 14 | |
| 17 | 2000 | 13 | |
| 18 | 2010 | 12 | |
| 19 | 2006 | 11 | |
| 20 | 2002 | 6 |
About Fangyan Dai
Fangyan Dai is a scholar working on Molecular Biology, Hematology, Cell Biology, Pathology and Forensic Medicine and Epidemiology, having authored 25 papers that have together received 1.4k indexed citations. Recurring topics across this work include TGF-β signaling in diseases (7 papers), Acute Myeloid Leukemia Research (4 papers), Chronic Myeloid Leukemia Treatments (3 papers), Nuclear Structure and Function (3 papers), Glycosylation and Glycoproteins Research (2 papers), Ubiquitin and proteasome pathways (2 papers), Endoplasmic Reticulum Stress and Disease (2 papers) and Single-cell and spatial transcriptomics (2 papers). The work is most often cited by research in Management Science and Operations Research (326 citations), Cancer Research (126 citations), Molecular Biology (608 citations), Finance (89 citations) and Cell Biology (134 citations). Fangyan Dai has collaborated with scholars based in United States, China and Taiwan. Frequent co-authors include Kai Chen, Yi Zhou, Xia Lin, Xin‐Hua Feng, Chenbei Chang, Hua He, Long Yu, Shouyuan Zhao, Yongjing Chen and Yongjun Dang. Their work appears in journals such as Developmental Cell, Blood, Journal of Cell Science, Genomics and EMBO Reports.
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.