Qiu-xiang Du

405 total citations
35 papers, 257 citations indexed

About

Qiu-xiang Du is a scholar working on Molecular Biology, Insect Science and Rehabilitation. According to data from OpenAlex, Qiu-xiang Du has authored 35 papers receiving a total of 257 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 13 papers in Insect Science and 11 papers in Rehabilitation. Recurrent topics in Qiu-xiang Du's work include Forensic Entomology and Diptera Studies (13 papers), Wound Healing and Treatments (11 papers) and Molecular Biology Techniques and Applications (9 papers). Qiu-xiang Du is often cited by papers focused on Forensic Entomology and Diptera Studies (13 papers), Wound Healing and Treatments (11 papers) and Molecular Biology Techniques and Applications (9 papers). Qiu-xiang Du collaborates with scholars based in China, United States and Lithuania. Qiu-xiang Du's co-authors include Junhong Sun, Yingyuan Wang, Rufeng Bai, Qianqian Jin, Na Li, Jie Cao, Xiaojun Lu, Sanqiang Li, Xin‐hua Liang and Haoliang Fan and has published in prestigious journals such as SHILAP Revista de lepidopterología, Analytical Chemistry and Talanta.

In The Last Decade

Qiu-xiang Du

33 papers receiving 257 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qiu-xiang Du China 10 113 98 61 36 30 35 257
Jianlong Ma China 11 165 1.5× 75 0.8× 3 0.0× 78 2.2× 3 0.1× 32 333
Junlin Huang China 10 46 0.4× 30 0.3× 2 0.0× 28 0.8× 22 0.7× 35 224
Dongqing Zhu China 12 96 0.8× 12 0.1× 4 0.1× 17 0.5× 5 0.2× 32 355
Chenyue Lu United States 8 29 0.3× 31 0.3× 5 0.1× 7 0.2× 2 0.1× 13 183
Guo Li China 10 50 0.4× 4 0.0× 4 0.1× 9 0.3× 8 0.3× 35 225
Agnieszka Kamińska Poland 11 229 2.0× 3 0.0× 6 0.1× 82 2.3× 6 0.2× 18 333
Man Rao China 12 173 1.5× 4 0.0× 2 0.0× 22 0.6× 3 0.1× 27 489
Abbas Habibalahi Australia 10 38 0.3× 12 0.1× 10 0.3× 6 0.2× 23 233
Shuo Xu China 11 123 1.1× 12 0.1× 46 1.3× 3 0.1× 18 311
John S. Chorba United States 8 70 0.6× 2 0.0× 6 0.1× 12 0.3× 5 0.2× 15 224

Countries citing papers authored by Qiu-xiang Du

Since Specialization
Citations

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

Fields of papers citing papers by Qiu-xiang Du

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Qiu-xiang Du. 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 Qiu-xiang Du. The network helps show where Qiu-xiang Du may publish in the future.

Co-authorship network of co-authors of Qiu-xiang Du

This figure shows the co-authorship network connecting the top 25 collaborators of Qiu-xiang Du. A scholar is included among the top collaborators of Qiu-xiang Du 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 Qiu-xiang Du. Qiu-xiang Du 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.
Gao, Yu, et al.. (2025). Artificial intelligence in forensic pathology: Multi-organ postmortem pathomics for estimating postmortem interval. Computer Methods and Programs in Biomedicine. 270. 108949–108949.
2.
Shen, Junyi, Shidong Zhou, Liangliang Wang, et al.. (2024). Development of a screening system of gene sets for estimating the time of early skeletal muscle injury based on second-generation sequencing technology. International Journal of Legal Medicine. 138(4). 1629–1644. 1 indexed citations
3.
Li, Jian, Mingfeng Liu, Na Li, et al.. (2023). Multi-omics integration strategy in the post-mortem interval of forensic science. Talanta. 268(Pt 1). 125249–125249. 15 indexed citations
4.
Li, Na, Shidong Zhou, Jian Li, et al.. (2023). Exploring postmortem succession of rat intestinal microbiome for PMI based on machine learning algorithms and potential use for humans. Forensic Science International Genetics. 66. 102904–102904. 13 indexed citations
5.
Cao, Jie, Jian Li, Kang Ren, et al.. (2023). Combined metabolomics and tandem machine-learning models for wound age estimation: a novel analytical strategy. Forensic Sciences Research. 8(1). 50–61. 3 indexed citations
6.
Li, Jian, Mingfeng Liu, Na Li, et al.. (2023). Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA. Diagnostics. 13(3). 395–395. 3 indexed citations
7.
Li, Na, Xin‐hua Liang, Qianqian Jin, et al.. (2023). Novel ratio-expressions of genes enables estimation of wound age in contused skeletal muscle. International Journal of Legal Medicine. 138(1). 197–206. 1 indexed citations
8.
Jin, Qianqian, et al.. (2023). Postmortem Interval Estimation Using Protein Chip Technology Combined with Multivariate Analysis Methods.. SHILAP Revista de lepidopterología. 39(2). 115–120. 1 indexed citations
9.
Gu, Zhen, et al.. (2022). Application of Linear Regression Model of Gpnmb Gene in Rat Injury Time Estimation.. SHILAP Revista de lepidopterología. 38(4). 468–472. 1 indexed citations
10.
Ren, Kang, Liangliang Wang, Yifei Wang, et al.. (2022). Wound age estimation based on next-generation sequencing: Fitting the optimal index system using machine learning. Forensic Science International Genetics. 59. 102722–102722. 2 indexed citations
11.
Lu, Xiaojun, Jian Li, Qiu-xiang Du, et al.. (2022). A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques. International Journal of Legal Medicine. 137(1). 237–249. 19 indexed citations
12.
Li, Na, Rufeng Bai, Chun Li, et al.. (2021). Insight into molecular profile changes after skeletal muscle contusion using microarray and bioinformatics analyses. Bioscience Reports. 41(1). 11 indexed citations
13.
Ren, Kang, Liangliang Wang, Liang Wang, et al.. (2021). Investigating Transcriptional Dynamics Changes and Time-Dependent Marker Gene Expression in the Early Period After Skeletal Muscle Injury in Rats. Frontiers in Genetics. 12. 650874–650874. 9 indexed citations
14.
Du, Qiu-xiang, et al.. (2021). Estimating the time of skeletal muscle contusion based on the spatial distribution of neutrophils: a practical approach to forensic problems. International Journal of Legal Medicine. 136(1). 149–158. 3 indexed citations
15.
Gu, Zhen, et al.. (2020). Analysis of sensitivity and specificity: precise recognition of neutrophils during regeneration of contused skeletal muscle in rats. Forensic Sciences Research. 7(2). 228–237. 8 indexed citations
16.
Cao, Jie, et al.. (2020). Novel insights into wound age estimation: combined with “up, no change, or down” system and cosine similarity in python environment. International Journal of Legal Medicine. 134(6). 2177–2186. 6 indexed citations
17.
Li, Na, Qiu-xiang Du, Rufeng Bai, & Junhong Sun. (2018). Vitality and wound-age estimation in forensic pathology: review and future prospects. Forensic Sciences Research. 5(1). 15–24. 38 indexed citations
18.
Sun, Junhong, et al.. (2017). An “up, no change, or down” system: Time-dependent expression of mRNAs in contused skeletal muscle of rats used for wound age estimation. Forensic Science International. 272. 104–110. 12 indexed citations
19.
20.
Du, Qiu-xiang, Junhong Sun, Lingyu Zhang, et al.. (2013). Time-dependent expression of SNAT2 mRNA in the contused skeletal muscle of rats: a possible marker for wound age estimation. Forensic Science Medicine and Pathology. 9(4). 528–533. 17 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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