Xiaolin Wan

3.0k total citations · 2 hit papers
28 papers, 2.4k citations indexed

About

Xiaolin Wan is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine and Oncology. According to data from OpenAlex, Xiaolin Wan has authored 28 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 9 papers in Pulmonary and Respiratory Medicine and 5 papers in Oncology. Recurrent topics in Xiaolin Wan's work include Sarcoma Diagnosis and Treatment (8 papers), PI3K/AKT/mTOR signaling in cancer (6 papers) and Cell Adhesion Molecules Research (4 papers). Xiaolin Wan is often cited by papers focused on Sarcoma Diagnosis and Treatment (8 papers), PI3K/AKT/mTOR signaling in cancer (6 papers) and Cell Adhesion Molecules Research (4 papers). Xiaolin Wan collaborates with scholars based in United States, China and Canada. Xiaolin Wan's co-authors include Lee J. Helman, Chand Khanna, Arnulfo Mendoza, Patrick J. Grohar, Choh Yeung, Stephen M. Hewitt, Osarenoma Olomu, Richard Görlick, Ryan D. Cassaday and Na Shen and has published in prestigious journals such as Advanced Materials, Nature Medicine and Advanced Functional Materials.

In The Last Decade

Xiaolin Wan

26 papers receiving 2.4k citations

Hit Papers

Rapamycin induces feedback activation of Akt signaling th... 2004 2026 2011 2018 2006 2004 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaolin Wan United States 17 1.5k 742 549 420 300 28 2.4k
Arnulfo Mendoza United States 26 1.5k 1.0× 789 1.1× 965 1.8× 636 1.5× 312 1.0× 51 2.8k
Barbara Sennino United States 28 2.0k 1.4× 698 0.9× 1.1k 2.0× 817 1.9× 121 0.4× 47 3.8k
Makoto Saegusa Japan 38 1.8k 1.2× 565 0.8× 1.3k 2.4× 680 1.6× 199 0.7× 163 3.8k
Emiliano Cocco United States 32 1.1k 0.7× 833 1.1× 1.6k 2.8× 532 1.3× 150 0.5× 83 3.2k
Inge H. Briaire‐de Bruijn Netherlands 29 1.0k 0.7× 1.1k 1.5× 714 1.3× 467 1.1× 226 0.8× 65 2.7k
Raushan T. Kurmasheva United States 27 1.3k 0.9× 546 0.7× 814 1.5× 366 0.9× 315 1.1× 120 2.3k
Benoît Lhermitte France 18 821 0.6× 702 0.9× 710 1.3× 647 1.5× 187 0.6× 77 2.5k
Aasmund Berner Norway 33 1.2k 0.8× 873 1.2× 960 1.7× 582 1.4× 73 0.2× 99 3.3k
Ralph M. Wirtz Germany 31 2.1k 1.5× 520 0.7× 1.3k 2.4× 780 1.9× 103 0.3× 151 3.8k
Masato Kochi Japan 31 907 0.6× 660 0.9× 468 0.9× 413 1.0× 672 2.2× 86 4.5k

Countries citing papers authored by Xiaolin Wan

Since Specialization
Citations

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

Fields of papers citing papers by Xiaolin Wan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaolin Wan

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaolin Wan. A scholar is included among the top collaborators of Xiaolin Wan 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 Xiaolin Wan. Xiaolin Wan 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.
Wan, Xiaolin, et al.. (2025). Higher-order band topology in a twisted bilayer kagome lattice. Physical review. B.. 111(8).
3.
Xie, Hongyao, Xiaolin Wan, Yasong Wu, et al.. (2024). Topological Electronic Transition Contributing to Improved Thermoelectric Performance in p‐Type Mg3Sb2−xBix Solid Solutions. Advanced Materials. 36(26). e2400845–e2400845. 21 indexed citations
4.
He, Ling, et al.. (2023). SIRT4 in ageing. Biogerontology. 24(3). 347–362. 13 indexed citations
5.
Yohe, Marielle E., Kai Pollard, Angelina V. Vaseva, et al.. (2022). Targeting farnesylation as a novel therapeutic approach in HRAS-mutant rhabdomyosarcoma. Oncogene. 41(21). 2973–2983. 28 indexed citations
6.
Chen, Jieqiong, et al.. (2022). Cognitive Impairment in Phenotypic Leber Hereditary Optic Neuropathy Caused by Mutation in Nuclear Gene NDUFAF5. Journal of Neuro-Ophthalmology. 44(1). e20–e22.
7.
Luo, Qinghua, Xiaolin Wan, Bing‐Xing Pan, et al.. (2019). NEMO-binding domain peptides alleviate perihematomal inflammation injury after experimental intracerebral hemorrhage. Neuroscience. 409. 43–57. 7 indexed citations
8.
Heske, Christine M., Choh Yeung, Arnulfo Mendoza, et al.. (2016). The Role of PDGFR-β Activation in Acquired Resistance to IGF-1R Blockade in Preclinical Models of Rhabdomyosarcoma. Translational Oncology. 9(6). 540–547. 10 indexed citations
9.
Wan, Xiaolin, Choh Yeung, Christine M. Heske, Arnulfo Mendoza, & Lee J. Helman. (2015). IGF-1R Inhibition Activates a YES/SFK Bypass Resistance Pathway: Rational Basis for Co-Targeting IGF-1R and Yes/SFK Kinase in Rhabdomyosarcoma. Neoplasia. 17(4). 358–366. 40 indexed citations
10.
Yeung, Choh, Vu N. Ngo, Patrick J. Grohar, et al.. (2013). Loss-of-function screen in rhabdomyosarcoma identifies CRKL-YES as a critical signal for tumor growth. Oncogene. 32(47). 5429–5438. 36 indexed citations
11.
Kim, Su Young, Xiaolin Wan, & Lee J. Helman. (2009). Targeting IGF-1R in the treatment of sarcomas: past, present and future. Bulletin du Cancer. 96(7). E52–E60. 16 indexed citations
12.
Wan, Xiaolin, So Young Kim, Lillian M. Guenther, et al.. (2009). Beta4 integrin promotes osteosarcoma metastasis and interacts with ezrin. Oncogene. 28(38). 3401–3411. 62 indexed citations
13.
Cao, Liang, Yunkai Yu, Duane Currier, et al.. (2008). Addiction to Elevated Insulin-like Growth Factor I Receptor and Initial Modulation of the AKT Pathway Define the Responsiveness of Rhabdomyosarcoma to the Targeting Antibody. Cancer Research. 68(19). 8039–8048. 122 indexed citations
14.
Petricoin, Emanuel F., Virginia Espina, Robyn P. Araujo, et al.. (2007). Phosphoprotein Pathway Mapping: Akt/Mammalian Target of Rapamycin Activation Is Negatively Associated with Childhood Rhabdomyosarcoma Survival. Cancer Research. 67(7). 3431–3440. 196 indexed citations
15.
Yeung, Choh, et al.. (2007). Evaluation of combined insulin-like growth factor type 1 (IGF1R) and mTOR pathway blockade in sarcoma xenograft models. Cancer Research. 67. 4760–4760. 1 indexed citations
16.
Wan, Xiaolin, Na Shen, Arnulfo Mendoza, Chand Khanna, & Lee J. Helman. (2006). CCI-779 Inhibits Rhabdomyosarcoma Xenograft Growth by an Antiangiogenic Mechanism Linked to the Targeting of mTOR/Hif-1α/VEGF Signaling. Neoplasia. 8(5). 394–401. 122 indexed citations
17.
Wan, Xiaolin, et al.. (2006). Rapamycin induces feedback activation of Akt signaling through an IGF-1R-dependent mechanism. Oncogene. 26(13). 1932–1940. 630 indexed citations breakdown →
18.
Khanna, Chand, Xiaolin Wan, Ryan D. Cassaday, et al.. (2004). The membrane-cytoskeleton linker ezrin is necessary for osteosarcoma metastasis. Nature Medicine. 10(2). 182–186. 572 indexed citations breakdown →
19.
20.
Wan, Xiaolin, et al.. (2001). Acidic fibroblast growth factor overexpression partially protects 3T3 fibroblasts from apoptosis induced by synthetic retinoid CD437. Journal of Molecular Medicine. 79(2-3). 143–148. 8 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026