Xuexia Wang

1.5k total citations
54 papers, 726 citations indexed

About

Xuexia Wang is a scholar working on Genetics, Molecular Biology and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Xuexia Wang has authored 54 papers receiving a total of 726 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Genetics, 16 papers in Molecular Biology and 7 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Xuexia Wang's work include Genetic Associations and Epidemiology (21 papers), Genetic Mapping and Diversity in Plants and Animals (8 papers) and Bioinformatics and Genomic Networks (7 papers). Xuexia Wang is often cited by papers focused on Genetic Associations and Epidemiology (21 papers), Genetic Mapping and Diversity in Plants and Animals (8 papers) and Bioinformatics and Genomic Networks (7 papers). Xuexia Wang collaborates with scholars based in United States, China and Germany. Xuexia Wang's co-authors include Qiuying Sha, Shuanglin Zhang, Chunxiao Jiang, Yong Ren, Mingyao Li, Jian Wang, Hsiao‐Hwa Chen, Xiaodong Wang, Håkon Håkonarson and Huaizhen Qin and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Xuexia Wang

50 papers receiving 717 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xuexia Wang United States 15 195 191 109 95 88 54 726
Dong Kyu Kim South Korea 17 29 0.1× 158 0.8× 49 0.4× 139 1.5× 79 0.9× 74 726
Andrew Macdonald United Kingdom 14 46 0.2× 339 1.8× 24 0.2× 118 1.2× 220 2.5× 25 1.3k
R. Kudo Japan 15 107 0.5× 289 1.5× 124 1.1× 110 1.2× 23 0.3× 70 908
Takanori Watanabe Japan 14 64 0.3× 129 0.7× 47 0.4× 68 0.7× 72 0.8× 62 626
Jin Liu China 12 20 0.1× 195 1.0× 47 0.4× 121 1.3× 91 1.0× 93 602
Chi‐Ching Chang Taiwan 17 55 0.3× 173 0.9× 50 0.5× 115 1.2× 44 0.5× 54 908
Tianbao Wang China 13 114 0.6× 104 0.5× 31 0.3× 122 1.3× 66 0.8× 52 715
Daming Wang China 14 23 0.1× 223 1.2× 15 0.1× 79 0.8× 151 1.7× 84 812
Qingcai Wang China 15 105 0.5× 252 1.3× 66 0.6× 49 0.5× 190 2.2× 44 661
Peipei Zhou China 19 35 0.2× 294 1.5× 67 0.6× 107 1.1× 17 0.2× 63 963

Countries citing papers authored by Xuexia Wang

Since Specialization
Citations

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

Fields of papers citing papers by Xuexia Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xuexia Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Xuexia Wang. A scholar is included among the top collaborators of Xuexia 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 Xuexia Wang. Xuexia Wang 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.
Wang, Yue, Haibo Xu, Xuexia Wang, et al.. (2025). Achieving overall low greenhouse gas and ammonia emissions from digested pig slurry during storage and after field application by maintaining a micro-oxygen level at the storage stage. Journal of environmental chemical engineering. 13(3). 116712–116712.
2.
Singh, Purnima, David K. Crossman, Changde Cheng, et al.. (2025). Alternative mRNA splicing in anthracycline-induced cardiomyopathy – a COG-ALTE03N1 report. Cardio-Oncology. 11(1). 47–47.
3.
Yan, Qi, Di He, Douglas I. Walker, et al.. (2023). The neonatal blood spot metabolome in retinoblastoma. SHILAP Revista de lepidopterología. 2. 100123–100123. 5 indexed citations
4.
Sharafeldin, Noha, Purnima Singh, David K. Crossman, et al.. (2023). Gene-Level Analysis of Anthracycline-Induced Cardiomyopathy in Cancer Survivors. JACC CardioOncology. 5(6). 807–818. 5 indexed citations
5.
Singh, Purnima, David K. Crossman, Xuexia Wang, et al.. (2023). Haptoglobin Gene Expression and Anthracycline-Related Cardiomyopathy in Childhood Cancer Survivors. JACC CardioOncology. 5(3). 392–401. 6 indexed citations
6.
Liang, Xiaoyu, et al.. (2023). A gene based combination test using GWAS summary data. BMC Bioinformatics. 24(1). 2–2. 2 indexed citations
7.
Caliebe, Amke, Fasil Tekola‐Ayele, Burcu F. Darst, et al.. (2022). Including diverse and admixed populations in genetic epidemiology research. Genetic Epidemiology. 46(7). 347–371. 26 indexed citations
8.
Wang, Xuexia, et al.. (2022). Gene-based association tests using GWAS summary statistics and incorporating eQTL. Scientific Reports. 12(1). 3553–3553. 1 indexed citations
9.
Wang, Yabo, et al.. (2022). The Clinical Efficacy of Microsuture Technique Combined With Voice Therapy in Patients with Reinke's Edema. Journal of Voice. 39(3). 811–815. 1 indexed citations
10.
Wang, Jianyong, et al.. (2021). Prognostic Factors in Acute Myeloid Leukemia with t(8;21)/AML1-ETO: Strategies to Define High-Risk Patients. Indian Journal of Hematology and Blood Transfusion. 38(4). 631–637. 3 indexed citations
11.
12.
Guo, Xuan, et al.. (2020). TS: a powerful truncated test to detect novel disease associated genes using publicly available gWAS summary data. BMC Bioinformatics. 21(1). 172–172. 3 indexed citations
13.
Tintle, Nathan, David W. Fardo, Mariza de Andrade, et al.. (2018). GAW20: methods and strategies for the new frontiers of epigenetics and pharmacogenomics. BMC Proceedings. 12(S9). 26–26. 2 indexed citations
14.
Zhu, Huanhuan, Zhenchuan Wang, Xuexia Wang, & Qiuying Sha. (2016). A novel statistical method for rare-variant association studies in general pedigrees. BMC Proceedings. 10(S7). 193–196. 2 indexed citations
15.
Wang, Xuexia, Rui Xiao, Xiaofeng Zhu, & Mingyao Li. (2016). Gene Mapping in Admixed Families: A Cautionary Note on the Interpretation of the Transmission Disequilibrium Test and a Possible Solution. Human Heredity. 81(2). 106–116. 3 indexed citations
16.
Wang, Xuexia, Xingwang Zhao, & Jin Zhou. (2016). Testing rare variants for hypertension using family-based tests with different weighting schemes. BMC Proceedings. 10(S7). 233–237. 6 indexed citations
17.
Tate, Everett, et al.. (2014). Developmental exposure to 2,3,7,8 tetrachlorodibenzo-p-dioxin attenuates capacity of hematopoietic stem cells to undergo lymphocyte differentiation. Toxicology and Applied Pharmacology. 277(2). 172–182. 16 indexed citations
18.
Ferguson, Jane F., Christine Hinkle, Nehal N. Mehta, et al.. (2012). Translational Studies of Lipoprotein-Associated Phospholipase A2 in Inflammation and Atherosclerosis. Journal of the American College of Cardiology. 59(8). 764–772. 39 indexed citations
19.
Wang, Xuexia, Huaizhen Qin, & Qiuying Sha. (2009). Incorporating multiple-marker information to detect risk loci for rheumatoid arthritis. BMC Proceedings. 3(S7). S28–S28. 6 indexed citations
20.
Wang, Xuexia, Zhaogong Zhang, Shuanglin Zhang, & Qiuying Sha. (2007). Genome-wide association tests by two-stage approaches with unified analysis of families and unrelated individuals. BMC Proceedings. 1(S1). S140–S140. 3 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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