Qiuying Sha

987 total citations
80 papers, 645 citations indexed

About

Qiuying Sha is a scholar working on Genetics, Molecular Biology and Epidemiology. According to data from OpenAlex, Qiuying Sha has authored 80 papers receiving a total of 645 indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Genetics, 35 papers in Molecular Biology and 4 papers in Epidemiology. Recurrent topics in Qiuying Sha's work include Genetic Associations and Epidemiology (64 papers), Genetic Mapping and Diversity in Plants and Animals (29 papers) and Bioinformatics and Genomic Networks (24 papers). Qiuying Sha is often cited by papers focused on Genetic Associations and Epidemiology (64 papers), Genetic Mapping and Diversity in Plants and Animals (29 papers) and Bioinformatics and Genomic Networks (24 papers). Qiuying Sha collaborates with scholars based in United States, China and Austria. Qiuying Sha's co-authors include Shuanglin Zhang, Xuexia Wang, Huann‐Sheng Chen, Zhenchuan Wang, Zhaogong Zhang, Xiaodong Wang, Tao Feng, Xiaoyu Liang, Huanhuan Zhu and Xiaofeng Zhu and has published in prestigious journals such as Bioinformatics, PLoS ONE and Scientific Reports.

In The Last Decade

Qiuying Sha

74 papers receiving 636 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qiuying Sha United States 14 515 282 43 33 27 80 645
Jennifer L. Asimit United Kingdom 11 397 0.8× 206 0.7× 22 0.5× 15 0.5× 17 0.6× 22 501
Yan Gong China 10 100 0.2× 306 1.1× 40 0.9× 15 0.5× 26 1.0× 18 497
Matthew Flickinger United States 5 233 0.5× 217 0.8× 32 0.7× 11 0.3× 21 0.8× 6 421
Cheryl DeScipio United States 15 334 0.6× 337 1.2× 23 0.5× 8 0.2× 35 1.3× 18 834
Alina Khromykh United States 10 164 0.3× 202 0.7× 28 0.7× 12 0.4× 30 1.1× 13 367
Josef Davidsson Sweden 11 165 0.3× 191 0.7× 14 0.3× 13 0.4× 40 1.5× 16 442
Heide Seidel Germany 14 192 0.4× 182 0.6× 36 0.8× 14 0.4× 42 1.6× 24 337
Emmanouil Manolakos Greece 15 342 0.7× 181 0.6× 24 0.6× 8 0.2× 23 0.9× 70 642
Laura A. Crinnion United Kingdom 13 160 0.3× 274 1.0× 15 0.3× 17 0.5× 15 0.6× 26 413
Robert J. Hardwick United Kingdom 6 268 0.5× 270 1.0× 19 0.4× 8 0.2× 24 0.9× 8 494

Countries citing papers authored by Qiuying Sha

Since Specialization
Citations

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

Fields of papers citing papers by Qiuying Sha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qiuying Sha

This figure shows the co-authorship network connecting the top 25 collaborators of Qiuying Sha. A scholar is included among the top collaborators of Qiuying Sha 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 Qiuying Sha. Qiuying Sha 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.
Larsen, Kristoffer, Zhuo He, Xinwei Zhang, et al.. (2025). A New Method Using Deep Learning to Predict the Response to Cardiac Resynchronization Therapy. Journal of Imaging Informatics in Medicine. 38(6). 4029–4045.
2.
Chen, Zhao, Zhengming Ding, Qiuying Sha, et al.. (2024). CLCLSA: Cross-omics linked embedding with contrastive learning and self attention for integration with incomplete multi-omics data. Computers in Biology and Medicine. 170. 108058–108058. 9 indexed citations
3.
Keyak, Joyce H., Sigurður Sigurdsson, Zhao Chen, et al.. (2024). A new hip fracture risk index derived from FEA-computed proximal femur fracture loads and energies-to-failure. Osteoporosis International. 35(5). 785–794. 2 indexed citations
4.
5.
Zhang, Ling, Mingxia Zhao, Cheng He, et al.. (2023). TGPred: efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning and optimization. NAR Genomics and Bioinformatics. 5(3). lqad083–lqad083.
6.
Zhang, Shuanglin, et al.. (2023). A clustering linear combination method for multiple phenotype association studies based on GWAS summary statistics. Scientific Reports. 13(1). 3389–3389. 1 indexed citations
7.
Zhang, Shuanglin, et al.. (2023). Joint analysis of multiple phenotypes for extremely unbalanced case‐control association studies. Genetic Epidemiology. 47(2). 185–197. 2 indexed citations
8.
Sha, Qiuying, et al.. (2020). Testing gene-environment interactions for rare and/or common variants in sequencing association studies. PLoS ONE. 15(3). e0229217–e0229217. 3 indexed citations
9.
Wang, Zhenchuan, Qiuying Sha, & Shuanglin Zhang. (2016). Joint Analysis of Multiple Traits Using "Optimal" Maximum Heritability Test. PLoS ONE. 11(3). e0150975–e0150975. 20 indexed citations
10.
Liang, Xiaoyu, Zhenchuan Wang, Qiuying Sha, & Shuanglin Zhang. (2016). An Adaptive Fisher’s Combination Method for Joint Analysis of Multiple Phenotypes in Association Studies. Scientific Reports. 6(1). 34323–34323. 24 indexed citations
11.
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
12.
Zhu, Huanhuan, Shuanglin Zhang, & Qiuying Sha. (2015). Power Comparisons of Methods for Joint Association Analysis of Multiple Phenotypes. Human Heredity. 80(3). 144–152. 11 indexed citations
13.
Sha, Qiuying, et al.. (2014). Detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data. BMC Proceedings. 8(S1). S91–S91. 5 indexed citations
14.
Sha, Qiuying, Zhaogong Zhang, & Shuanglin Zhang. (2011). Joint Analysis for Genome-Wide Association Studies in Family-Based Designs. PLoS ONE. 6(7). e21957–e21957. 7 indexed citations
15.
Zhang, Zhaogong, et al.. (2009). Application of seventeen two-locus models in genome-wide association studies by two-stage strategy. BMC Proceedings. 3(S7). S26–S26. 4 indexed citations
16.
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
17.
Sha, Qiuying, Huann‐Sheng Chen, & Shuanglin Zhang. (2007). A new association test using haplotype similarity. Genetic Epidemiology. 31(6). 577–593. 21 indexed citations
18.
Feng, Tao, Shuanglin Zhang, & Qiuying Sha. (2007). A method dealing with a large number of correlated traits in a linkage genome scan. BMC Proceedings. 1(S1). S84–S84. 3 indexed citations
19.
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
20.
Zhang, Shuanglin, et al.. (2004). Reply to Knapp and Becker. The American Journal of Human Genetics. 74(3). 591–593. 2 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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