Longyun Fang
- Molecular Biology top 5%
- Machine Learning in Bioinformatics 8
- RNA and protein synthesis mechanisms 8
- Genomics and Phylogenetic Studies 6
- Genomics and Chromatin Dynamics 1
- Hedgehog Signaling Pathway Studies 1
- Cancer Research top 10%
- Microbiology top 10%
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- Hippo pathway signaling and YAP/TAZ 2
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- Bone Tissue Engineering Materials 1
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- Neuroendocrine regulation and behavior 1
- Journals
- Bioinformatics (2 papers)Molecular Genetics and Genomics (1 paper)Experimental and Molecular Pathology (1 paper)
- Partner nations
- ChinaSaudi ArabiaUnited States
In The Last Decade
Longyun Fang
14 papers receiving 2.1k citations
Hit Papers
Peers
Comparison fields: 5 of 91
- Molecular Biology 1.9k
- Cancer Research 227
- Microbiology 63
- Computational Theory and Mathematics 130
- Immunology and Allergy 36
Countries citing papers authored by Longyun Fang
This map shows the geographic impact of Longyun Fang'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 Longyun Fang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Longyun Fang more than expected).
Fields of papers citing papers by Longyun Fang
This network shows the impact of papers produced by Longyun Fang. 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 Longyun Fang. The network helps show where Longyun Fang may publish in the future.
Co-authorship network
The 24 scholars most cited alongside Longyun Fang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2019 | 11 | |
| 3 | 2017 | 71 | |
| 4 | 2017 | 36 | |
| 5 | 2016 | 1 | |
| 6 | 2015 | 62 | |
| 7 | 2015 | 221 | |
| 8 | 2015 | 134 | |
| 9 | 2015 | 134 | |
| 10 | 2015 | 152 | |
| 11 | Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequencesbreakdown → | 2015 | 651 |
| 12 | 2015 | 320 | |
| 13 | 2014 | 239 | |
| 14 | Clinical significance of Mena and Her-2 expression in breast cancer. | 2012 | 10 |
| 15 | 2008 | 46 |
About Longyun Fang
Longyun Fang is a scholar working on Sensory Systems, Pharmacy, Immunology and Allergy, Molecular Biology and Cell Biology, having authored 15 papers that have together received 2.1k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (8 papers), RNA and protein synthesis mechanisms (8 papers), Genomics and Phylogenetic Studies (6 papers), Hippo pathway signaling and YAP/TAZ (2 papers), Genomics and Chromatin Dynamics (1 paper), Bone Tissue Engineering Materials (1 paper), Hedgehog Signaling Pathway Studies (1 paper) and Neuroendocrine regulation and behavior (1 paper). The work is most often cited by research in Molecular Biology (1.9k citations), Cancer Research (227 citations), Microbiology (63 citations), Computational Theory and Mathematics (130 citations) and Immunology and Allergy (36 citations). Longyun Fang has collaborated with scholars based in China, Saudi Arabia and United States. Frequent co-authors include Fule Liu, Bin Liu, Xiaolong Wang, Kuo‐Chen Chou, Junjie Chen, Kuo‐Chen Chou, Bin Liu, Xun Lan, Xiaolong Wang and Shanyi Wang. Their work appears in journals such as Bioinformatics, Molecular Genetics and Genomics, Experimental and Molecular Pathology, Journal of Theoretical Biology and Molecular BioSystems.
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.