Lin Fu

2.2k total citations
96 papers, 1.4k citations indexed

About

Lin Fu is a scholar working on Hematology, Molecular Biology and Cancer Research. According to data from OpenAlex, Lin Fu has authored 96 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Hematology, 52 papers in Molecular Biology and 30 papers in Cancer Research. Recurrent topics in Lin Fu's work include Acute Myeloid Leukemia Research (50 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (14 papers) and MicroRNA in disease regulation (13 papers). Lin Fu is often cited by papers focused on Acute Myeloid Leukemia Research (50 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (14 papers) and MicroRNA in disease regulation (13 papers). Lin Fu collaborates with scholars based in China, United States and Netherlands. Lin Fu's co-authors include Jinlong Shi, Zhiheng Cheng, Longzhen Cui, Yifan Pang, Xiaoyan Ke, Yan Liu, Tingting Qian, Liang Quan, Qingyi Zhang and Yifeng Dai and has published in prestigious journals such as Neuron, PLoS ONE and Journal of Agricultural and Food Chemistry.

In The Last Decade

Lin Fu

94 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lin Fu China 20 959 538 359 211 199 96 1.4k
Pia Ragno Italy 26 761 0.8× 937 1.7× 476 1.3× 448 2.1× 312 1.6× 53 1.8k
Lenka Sedlaříková Czechia 13 616 0.6× 289 0.5× 290 0.8× 277 1.3× 116 0.6× 33 1.0k
Lydia Santell United States 21 617 0.6× 486 0.9× 483 1.3× 123 0.6× 144 0.7× 31 1.4k
António Torres Spain 26 1.4k 1.5× 530 1.0× 550 1.5× 255 1.2× 252 1.3× 48 2.0k
Bin Fan United States 17 448 0.5× 218 0.4× 262 0.7× 248 1.2× 112 0.6× 52 1.0k
Luís A. Corchete Spain 22 820 0.9× 294 0.5× 534 1.5× 488 2.3× 153 0.8× 78 1.5k
Zejuan Li United States 22 2.1k 2.2× 1.4k 2.6× 260 0.7× 201 1.0× 177 0.9× 49 2.6k
Francesco Paolo Tambaro United States 16 789 0.8× 160 0.3× 294 0.8× 309 1.5× 187 0.9× 40 1.3k
Michael Wanzel Germany 16 1.2k 1.3× 245 0.5× 229 0.6× 578 2.7× 137 0.7× 25 1.7k

Countries citing papers authored by Lin Fu

Since Specialization
Citations

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

Fields of papers citing papers by Lin Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lin Fu

This figure shows the co-authorship network connecting the top 25 collaborators of Lin Fu. A scholar is included among the top collaborators of Lin Fu 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 Lin Fu. Lin Fu 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.
Zhang, Li, Yu Zeng, Lin Fu, et al.. (2025). Dynamic Changes in Microorganisms and Metabolites During Silage Fermentation of Whole Winter Wheat. Veterinary Sciences. 12(8). 708–708. 1 indexed citations
2.
Wei, Yaru, Boyan Zhang, Lin Fu, et al.. (2025). Sympathetic functional units encoded by genetically defined postganglionic neurons. Neuron. 114(3). 463–478.e7.
3.
Fu, Lin, Li Liu, Li Zhang, et al.. (2023). Effects of inoculation with active microorganisms derived from adult goats on growth performance, gut microbiota and serum metabolome in newborn lambs. Frontiers in Microbiology. 14. 1128271–1128271. 6 indexed citations
4.
Deng, Cong, Tiansheng Zeng, Pei Zhu, et al.. (2023). A novel 5-gene prognostic signature to improve risk stratification of cytogenetically normal acute myeloid leukemia. Journal of Cancer Research and Clinical Oncology. 149(12). 10015–10025. 2 indexed citations
5.
Zhang, Zhiyong, Cong Deng, Pei Zhu, et al.. (2023). Single‐cell RNA‐seq reveals a microenvironment and an exhaustion state of T/NK cells in acute myeloid leukemia. Cancer Science. 114(10). 3873–3883. 12 indexed citations
6.
Zhang, Li, Zhiyu Wang, Peng Zhou, et al.. (2022). Vitamin E supplementation improves post-transportation systemic antioxidant capacity in yak. PLoS ONE. 17(12). e0278660–e0278660. 8 indexed citations
7.
Deng, Cong, Chaozeng Si, Ye Xu, et al.. (2021). Prognostic significance of FSCN family in multiple myeloma. Journal of Cancer. 12(7). 1936–1944. 2 indexed citations
8.
Huang, Wenhui, Tingting Qian, Yan Liu, et al.. (2021). Increased expression of IFI16 predicts adverse prognosis in multiple myeloma. The Pharmacogenomics Journal. 21(4). 520–532. 4 indexed citations
9.
Huang, Wenhui, Tingting Qian, Zhiheng Cheng, et al.. (2020). Prognostic significance of Spinster homolog gene family in acute myeloid leukemia. Journal of Cancer. 11(15). 4581–4588. 10 indexed citations
10.
Hu, Ning, Yifan Pang, Hongmian Zhao, et al.. (2019). High expression of DOCK2 indicates good prognosis in acute myeloid leukemia. Journal of Cancer. 10(24). 6088–6094. 12 indexed citations
11.
Fu, Lin, Zhiheng Cheng, Fen Dong, et al.. (2019). Enhanced expression of FCER1G predicts positive prognosis in multiple myeloma. Journal of Cancer. 11(5). 1182–1194. 27 indexed citations
12.
Cheng, Zhiheng, Lei Zhou, Kai Hu, et al.. (2018). Prognostic significance of microRNA-99a in acute myeloid leukemia patients undergoing allogeneic hematopoietic stem cell transplantation. Bone Marrow Transplantation. 53(9). 1089–1095. 9 indexed citations
13.
Cheng, Zhiheng, Yifeng Dai, Yifan Pang, et al.. (2018). Clinical and Biological Implications of Mutational Spectrum in Acute Myeloid Leukemia of FAB Subtypes M0 and M1. Cellular Physiology and Biochemistry. 47(5). 1853–1861. 3 indexed citations
14.
Cheng, Zhiheng, Kai Hu, Lei Tian, et al.. (2018). Clinical and biological implications of mutational spectrum in acute myeloid leukemia of FAB subtypes M4 and M5. Cancer Gene Therapy. 25(3-4). 77–83. 11 indexed citations
15.
Zhang, Xinpei, Jinlong Shi, Jilei Zhang, et al.. (2018). Clinical and biological implications of <em>IDH1/2</em> in acute myeloid leukemia with <em>DNMT3A<sup>mut</sup></em>. Cancer Management and Research. Volume 10. 2457–2466. 8 indexed citations
16.
Cui, Longzhen, Zhiheng Cheng, Yan Liu, et al.. (2018). Overexpression of PDK2 and PDK3 reflects poor prognosis in acute myeloid leukemia. Cancer Gene Therapy. 27(1-2). 15–21. 41 indexed citations
17.
Cheng, Zhiheng, Yifeng Dai, Yifan Pang, et al.. (2018). Enhanced expressions of FHL2 and iASPP predict poor prognosis in acute myeloid leukemia. Cancer Gene Therapy. 26(1-2). 17–25. 19 indexed citations
18.
Zhang, Jilei, Xinpei Zhang, Gaoqi Zhang, et al.. (2018). High expression levels of SMAD3 and SMAD7 at diagnosis predict poor prognosis in acute myeloid leukemia patients undergoing chemotherapy. Cancer Gene Therapy. 26(5-6). 119–127. 12 indexed citations
19.
Jiang, Juhong, Yuanzhi Lu, Zhi Li, et al.. (2017). Ganetespib overcomes resistance to PARP inhibitors in breast cancer by targeting core proteins in the DNA repair machinery. Investigational New Drugs. 35(3). 251–259. 29 indexed citations
20.
Zhou, Lei, Qian Wang, Xiaosu Chen, et al.. (2016). AML1–ETO promotes SIRT1 expression to enhance leukemogenesis of t(8;21) acute myeloid leukemia. Experimental Hematology. 46. 62–69. 11 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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