Xiao‐Fan Wang

1.1k total citations · 1 hit paper
7 papers, 811 citations indexed

About

Xiao‐Fan Wang is a scholar working on Molecular Biology, Oncology and Nephrology. According to data from OpenAlex, Xiao‐Fan Wang has authored 7 papers receiving a total of 811 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 2 papers in Oncology and 1 paper in Nephrology. Recurrent topics in Xiao‐Fan Wang's work include TGF-β signaling in diseases (4 papers), Cancer-related gene regulation (2 papers) and Autophagy in Disease and Therapy (1 paper). Xiao‐Fan Wang is often cited by papers focused on TGF-β signaling in diseases (4 papers), Cancer-related gene regulation (2 papers) and Autophagy in Disease and Therapy (1 paper). Xiao‐Fan Wang collaborates with scholars based in United States, China and Singapore. Xiao‐Fan Wang's co-authors include Na Li, Nikolaos G. Frangogiannis, Carlos Gonzalez‐Quesada, Leonardo H. Mendoza, Marcin Dobaczewski, Marcin Bujak, Carolyn Wong, Joshua P. Frederick, Michael Datto and Jonathan M. Yingling and has published in prestigious journals such as Nature Communications, Nature Cell Biology and Circulation Research.

In The Last Decade

Xiao‐Fan Wang

7 papers receiving 800 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Cancer-cell-derived GABA promotes β-catenin-mediated tumour growth and immunosuppression

2022 165 citations De Huang, Yan Wang et al. Nature Cell Biology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Xiao‐Fan Wang United States	7	497	216	179	99	96	7	811
Qiang Sun China	14	339 0.7×	413 1.9×	230 1.3×	53 0.5×	143 1.5×	53	993
John Garcia United States	10	597 1.2×	553 2.6×	106 0.6×	36 0.4×	129 1.3×	16	1.3k
Olga Dashevsky United States	10	548 1.1×	112 0.5×	205 1.1×	120 1.2×	194 2.0×	20	858
Paola Portararo Italy	15	366 0.7×	136 0.6×	243 1.4×	357 3.6×	78 0.8×	23	947
Yasmine Zouggari France	7	359 0.7×	323 1.5×	90 0.5×	282 2.8×	108 1.1×	7	784
Wen Zhao China	14	314 0.6×	158 0.7×	198 1.1×	106 1.1×	107 1.1×	47	659
Emilia A. Korhonen Finland	9	389 0.8×	117 0.5×	240 1.3×	96 1.0×	110 1.1×	12	713
Young Shin Ryu South Korea	10	628 1.3×	153 0.7×	97 0.5×	67 0.7×	126 1.3×	14	869
Martin P. Powers United States	11	349 0.7×	61 0.3×	91 0.5×	49 0.5×	137 1.4×	18	805
Masaaki Matsumoto Japan	16	348 0.7×	81 0.4×	126 0.7×	113 1.1×	60 0.6×	64	786

Countries citing papers authored by Xiao‐Fan Wang

Since Specialization

Citations

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

Fields of papers citing papers by Xiao‐Fan Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiao‐Fan Wang. 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 Xiao‐Fan Wang. The network helps show where Xiao‐Fan Wang may publish in the future.

Co-authorship network of co-authors of Xiao‐Fan Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Xiao‐Fan Wang. A scholar is included among the top collaborators of Xiao‐Fan Wang 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 Xiao‐Fan Wang. Xiao‐Fan Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

7 of 7 papers shown

1.

Tan, Lianmei, Tao Yin, Handan Xiang, et al.. (2024). Aberrant cytoplasmic expression of UHRF1 restrains the MHC-I-mediated anti-tumor immune response. Nature Communications. 15(1). 8569–8569. 10 indexed citations

2.

Liang, Yaosi, Christopher C. Pan, Tao Yin, et al.. (2023). Branched‐Chain Amino Acid Accumulation Fuels the Senescence‐Associated Secretory Phenotype. Advanced Science. 11(2). e2303489–e2303489. 13 indexed citations

3.

Huang, De, Yan Wang, J. Will Thompson, et al.. (2022). Cancer-cell-derived GABA promotes β-catenin-mediated tumour growth and immunosuppression. Nature Cell Biology. 24(2). 230–241. 165 indexed citations breakdown →

4.

Krag, Søren, Carl Christian Danielsen, Xiao‐Fan Wang, et al.. (2013). Differential effects of Smad3 targeting in a murine model of chronic kidney disease. Physiological Reports. 1(7). e00181–e00181. 12 indexed citations

5.

Dobaczewski, Marcin, Marcin Bujak, Na Li, et al.. (2010). Smad3 Signaling Critically Regulates Fibroblast Phenotype and Function in Healing Myocardial Infarction. Circulation Research. 107(3). 418–428. 318 indexed citations

6.

Rich, Jeremy N., et al.. (2001). Transforming growth factor-? signaling in cancer. Microscopy Research and Technique. 52(4). 363–373. 48 indexed citations

7.

Yingling, Jonathan M., Michael Datto, Carolyn Wong, et al.. (1997). Tumor Suppressor Smad4 Is a Transforming Growth Factor β-Inducible DNA Binding Protein. Molecular and Cellular Biology. 17(12). 7019–7028. 245 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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