Xiao Xiao

12.1k total citations · 3 hit papers
200 papers, 7.5k citations indexed

About

Xiao Xiao is a scholar working on Molecular Biology, Genetics and Cellular and Molecular Neuroscience. According to data from OpenAlex, Xiao Xiao has authored 200 papers receiving a total of 7.5k indexed citations (citations by other indexed papers that have themselves been cited), including 124 papers in Molecular Biology, 110 papers in Genetics and 11 papers in Cellular and Molecular Neuroscience. Recurrent topics in Xiao Xiao's work include Virus-based gene therapy research (44 papers), Genetic Associations and Epidemiology (39 papers) and Genetics and Neurodevelopmental Disorders (26 papers). Xiao Xiao is often cited by papers focused on Virus-based gene therapy research (44 papers), Genetic Associations and Epidemiology (39 papers) and Genetics and Neurodevelopmental Disorders (26 papers). Xiao Xiao collaborates with scholars based in China, United States and Hong Kong. Xiao Xiao's co-authors include R. Jude Samulski, Juan Li, Juan Li, Jun Lin, Ming Li, Chengwen Li, Ziyong Cheng, Joseph E. Rabinowitz, Ping’an Ma and Shuang Liang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Chemical Society Reviews.

In The Last Decade

Xiao Xiao

191 papers receiving 7.4k citations

Hit Papers

Production of High-Titer Recombinant Adeno-Associated Vir... 1998 2026 2007 2016 1998 2002 2021 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiao Xiao China 42 4.7k 3.4k 984 654 633 200 7.5k
Frank Lezoualc’h France 39 7.1k 1.5× 2.2k 0.6× 559 0.6× 258 0.4× 387 0.6× 84 9.7k
Michael J. Caplan United States 57 6.8k 1.4× 2.0k 0.6× 444 0.5× 205 0.3× 648 1.0× 193 10.6k
Nadia Messaddeq France 49 5.7k 1.2× 1.5k 0.5× 349 0.4× 251 0.4× 1.1k 1.8× 126 9.6k
Shun’ichi Kuroda Japan 42 3.7k 0.8× 614 0.2× 744 0.8× 303 0.5× 356 0.6× 196 5.8k
Esther Vázquez Spain 47 4.0k 0.9× 1.0k 0.3× 620 0.6× 223 0.3× 538 0.8× 211 6.6k
Daniel Ory United States 39 5.4k 1.1× 2.9k 0.8× 265 0.3× 370 0.6× 1.1k 1.7× 110 10.4k
Keiya Ozawa Japan 60 6.3k 1.3× 2.8k 0.8× 378 0.4× 110 0.2× 2.1k 3.2× 339 12.1k
Roosmarijn E. Vandenbroucke Belgium 51 4.0k 0.8× 598 0.2× 901 0.9× 418 0.6× 919 1.5× 139 9.0k
Seng H. Cheng United States 65 8.2k 1.7× 3.0k 0.9× 311 0.3× 139 0.2× 816 1.3× 193 14.9k
Hu Li United States 46 7.0k 1.5× 992 0.3× 1.8k 1.8× 124 0.2× 970 1.5× 226 10.6k

Countries citing papers authored by Xiao Xiao

Since Specialization
Citations

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

Fields of papers citing papers by Xiao Xiao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiao Xiao

This figure shows the co-authorship network connecting the top 25 collaborators of Xiao Xiao. A scholar is included among the top collaborators of Xiao Xiao 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 Xiao. Xiao Xiao 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
2.
Wang, Ning, et al.. (2025). Glial cell crosstalk in Parkinson's disease: Mechanisms, implications, and therapeutic strategies. Fundamental Research. 5(6). 2960–2974. 6 indexed citations
3.
Zhang, Yue, Chuyi Zhang, Jing Yuan, et al.. (2025). Human mood disorder risk gene Synaptotagmin-14 contributes to mania-like behaviors in mice. Molecular Psychiatry. 30(8). 3466–3477. 1 indexed citations
4.
Chen, Songyue, Shumao Xu, Xiujun Fan, et al.. (2025). Advances in 2D materials for wearable biomonitoring. Materials Science and Engineering R Reports. 164. 100971–100971. 10 indexed citations
5.
Yang, Zhihui, Xin Cai, Chuyi Zhang, et al.. (2024). NEK4 modulates circadian fluctuations of emotional behaviors and synaptogenesis in male mice. Nature Communications. 15(1). 9180–9180. 1 indexed citations
6.
Wu, Yong, Chuyi Zhang, Xiao‐Lan Liu, et al.. (2024). Shared genetic architecture and causal relationship between sleep behaviors and lifespan. Translational Psychiatry. 14(1). 108–108. 2 indexed citations
7.
Xiao, Xiao, et al.. (2023). New “drugs and targets” in the GWAS era of bipolar disorder. Bipolar Disorders. 25(5). 410–421. 4 indexed citations
8.
Yang, Zhihui, Xin Cai, Zhongli Ding, et al.. (2023). Identification of a psychiatric risk gene NISCH at 3p21.1 GWAS locus mediating dendritic spine morphogenesis and cognitive function. BMC Medicine. 21(1). 254–254. 2 indexed citations
9.
Li, Ming, Tao Li, Xiao Xiao, et al.. (2022). Phenotypes, mechanisms and therapeutics: insights from bipolar disorder GWAS findings. Molecular Psychiatry. 27(7). 2927–2939. 17 indexed citations
10.
Wang, Junyang, Shiwu Li, Xiaoyan Li, et al.. (2022). Functional variant rs2270363 on 16p13.3 confers schizophrenia risk by regulating NMRAL1. Brain. 145(7). 2569–2585. 7 indexed citations
11.
Zhang, Yan, Chuyi Zhang, Jing Yuan, et al.. (2022). Epistatic interactions of NRG1 and ERBB4 on antipsychotic treatment response in first-episode schizophrenia patients. Schizophrenia Research. 241. 197–200. 3 indexed citations
12.
Xiao, Xiao, Rui Li, Cunjin Wu, et al.. (2022). A genome-wide association study identifies a novel association between SDC3 and apparent treatment-resistant hypertension. BMC Medicine. 20(1). 463–463. 3 indexed citations
13.
Jiang, Wei, et al.. (2021). Repeated Systemic Dosing of Adeno-Associated Virus Vectors in Immunocompetent Mice After Blockade of T Cell Costimulatory Pathways. Human Gene Therapy. 33(5-6). 290–300. 13 indexed citations
14.
Liang, Shuang, Xiao Xiao, Lixin Bai, et al.. (2021). Conferring Ti‐Based MOFs with Defects for Enhanced Sonodynamic Cancer Therapy. Advanced Materials. 33(18). e2100333–e2100333. 303 indexed citations breakdown →
15.
Xiao, Hui, Peipei Dang, Xiaohan Yun, et al.. (2020). Solvatochromic Photoluminescent Effects in All‐Inorganic Manganese(II)‐Based Perovskites by Highly Selective Solvent‐Induced Crystal‐to‐Crystal Phase Transformations. Angewandte Chemie International Edition. 60(7). 3699–3707. 111 indexed citations
16.
Yang, Yongfeng, Lu Wang, Lingyi Li, et al.. (2018). Genetic association and meta-analysis of a schizophrenia GWAS variant rs10489202 in East Asian populations. Translational Psychiatry. 8(1). 144–144. 7 indexed citations
17.
Vannoy, Charles H., Will Xiao, Peijuan Lu, Xiao Xiao, & Qi Long Lu. (2017). Efficacy of Gene Therapy Is Dependent on Disease Progression in Dystrophic Mice with Mutations in the FKRP Gene. Molecular Therapy — Methods & Clinical Development. 5. 31–42. 27 indexed citations
19.
Ou, Jianjun, Ming Li, & Xiao Xiao. (2016). The schizophrenia susceptibility gene ZNF804A confers risk of major mood disorders. The World Journal of Biological Psychiatry. 18(7). 557–562. 15 indexed citations
20.
Gérard, Catherine, Xiao Xiao, Mohammed Filali, et al.. (2014). An AAV9 coding for frataxin clearly improved the symptoms and prolonged the life of Friedreich ataxia mouse models. Molecular Therapy — Methods & Clinical Development. 1. 14044–14044. 40 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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