Rui Chang

2.9k total citations
41 papers, 586 citations indexed

About

Rui Chang is a scholar working on Molecular Biology, Computational Theory and Mathematics and Immunology. According to data from OpenAlex, Rui Chang has authored 41 papers receiving a total of 586 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 7 papers in Computational Theory and Mathematics and 7 papers in Immunology. Recurrent topics in Rui Chang's work include Bioinformatics and Genomic Networks (7 papers), Immunotherapy and Immune Responses (5 papers) and Bayesian Modeling and Causal Inference (5 papers). Rui Chang is often cited by papers focused on Bioinformatics and Genomic Networks (7 papers), Immunotherapy and Immune Responses (5 papers) and Bayesian Modeling and Causal Inference (5 papers). Rui Chang collaborates with scholars based in United States, China and United Kingdom. Rui Chang's co-authors include Wei Wang, Robert Shoemaker, Quan Chen, Kaili Ma, Qian Luo, Yushan Zhu, Linbo Chen, M. Stetter, Hongcheng Cheng and Chenglong Mu and has published in prestigious journals such as Journal of Biological Chemistry, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Rui Chang

36 papers receiving 570 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rui Chang United States 15 286 96 95 76 64 41 586
Zhenzhou Chen China 17 324 1.1× 66 0.7× 87 0.9× 59 0.8× 20 0.3× 53 812
Ernst R. Dow United States 16 480 1.7× 122 1.3× 162 1.7× 51 0.7× 123 1.9× 41 1.0k
Tülin Erşahin Türkiye 11 412 1.4× 85 0.9× 109 1.1× 69 0.9× 50 0.8× 16 777
Alberto Valdeolivas Germany 7 476 1.7× 96 1.0× 69 0.7× 28 0.4× 39 0.6× 13 679
Ammar Ammar Netherlands 7 357 1.2× 53 0.6× 52 0.5× 30 0.4× 38 0.6× 19 621
Yidan Zhang China 10 215 0.8× 114 1.2× 26 0.3× 33 0.4× 42 0.7× 45 466
Jianchao Wang China 19 214 0.7× 92 1.0× 163 1.7× 90 1.2× 68 1.1× 95 1.0k
Vasilis Ntranos United States 15 456 1.6× 215 2.2× 48 0.5× 37 0.5× 43 0.7× 28 1.0k
Cong Zheng China 14 215 0.8× 51 0.5× 61 0.6× 27 0.4× 67 1.0× 36 845

Countries citing papers authored by Rui Chang

Since Specialization
Citations

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

Fields of papers citing papers by Rui Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rui Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Rui Chang. A scholar is included among the top collaborators of Rui Chang 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 Rui Chang. Rui Chang 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.
Abbas, Yawar, Rohan B. Ambade, Muhammad Umair Khan, et al.. (2025). Sustainable high-pressure homogenization of hexagonal boron nitride for triboelectric nanogenerators: advancing self-powered environmental monitoring in portable electronics. Journal of Materials Chemistry A. 13(20). 14773–14785. 3 indexed citations
2.
3.
Wang, Mengzhen, Xinrui Li, Rui Chang, et al.. (2025). Photon-Counting Detector CTA in Standard- and Ultrahigh-Resolution Modes for Diagnosing Coronary Artery Stenosis Using Invasive Angiography as the Reference: A Prospective Study. American Journal of Roentgenology. 225(4). e2533021–e2533021.
4.
5.
He, Naying, Robert K.Y. Li, E. Mark Haacke, et al.. (2025). Increased diagnostic accuracy and better morphology characterization of unruptured intracranial aneurysm by ultra-high-resolution photon-counting detector CT angiography. Journal of NeuroInterventional Surgery. 18(3). 835–841. 2 indexed citations
6.
Rehman, Muhammad Muqeet, Yarjan Abdul Samad, Jahan Zeb Gul, et al.. (2025). 2D materials-memristive devices nexus: From status quo to Impending applications. Progress in Materials Science. 152. 101471–101471. 7 indexed citations
7.
Olney, Kimberly C., Aleksandra Wojtas, Michael DeTure, et al.. (2024). Distinct transcriptional alterations distinguish Lewy body disease from Alzheimer’s disease. Brain. 148(1). 69–88. 3 indexed citations
8.
Chang, Rui, et al.. (2024). The impact of manufacturing digital supply chain on supply chain disruption risks under uncertain environment—Based on dynamic capability perspective. Advanced Engineering Informatics. 60. 102385–102385. 21 indexed citations
9.
Cao, Lizhi, et al.. (2024). Elucidating cardiomyocyte heterogeneity and maturation dynamics through integrated single-cell and spatial transcriptomics. iScience. 28(1). 111596–111596. 2 indexed citations
10.
Zhu, Kuixi, Marc Henrion, Melissa Alamprese, et al.. (2023). Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer’s disease. Communications Biology. 6(1). 503–503. 7 indexed citations
11.
Jensen, Tanner, Jonathon Sens, Kuixi Zhu, et al.. (2022). Culture shock: microglial heterogeneity, activation, and disrupted single-cell microglial networks in vitro. Molecular Neurodegeneration. 17(1). 26–26. 49 indexed citations
12.
Gartrell, Robyn D., Andrew Chen, Emanuelle M. Rizk, et al.. (2020). Linking Transcriptomic and Imaging Data Defines Features of a Favorable Tumor Immune Microenvironment and Identifies a Combination Biomarker for Primary Melanoma. Cancer Research. 80(5). 1078–1087. 17 indexed citations
13.
Carcamo‐Orive, Ivan, Marc Henrion, Kuixi Zhu, et al.. (2020). Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness. PLoS Computational Biology. 16(12). e1008491–e1008491. 10 indexed citations
14.
Gartrell, Robyn D., Douglas K. Marks, Emanuelle M. Rizk, et al.. (2019). Validation of Melanoma Immune Profile (MIP), a Prognostic Immune Gene Prediction Score for Stage II–III Melanoma. Clinical Cancer Research. 25(8). 2494–2502. 17 indexed citations
15.
Song, Ziyi, Alus M. Xiaoli, Quanwei Zhang, et al.. (2017). Cyclin C regulates adipogenesis by stimulating transcriptional activity of CCAAT/enhancer-binding protein α. Journal of Biological Chemistry. 292(21). 8918–8932. 13 indexed citations
16.
Fu, Yichun, Yingzhi Qian, Shira Wieder, et al.. (2015). Immune biomarkers are more accurate in prediction of survival in ulcerated than in non-ulcerated primary melanomas. Cancer Immunology Immunotherapy. 64(9). 1193–1203. 15 indexed citations
17.
Bernardo, Sebastian, Марина Москаленко, Michael Pan, et al.. (2014). Defining the role of CD2 in disease progression and overall survival among patients with completely resected stage-II to -III cutaneous melanoma. Journal of the American Academy of Dermatology. 70(6). 1036–1044.e3. 15 indexed citations
18.
Bernardo, Sebastian, Марина Москаленко, Michael Pan, et al.. (2012). Elevated rates of transaminitis during ipilimumab therapy for metastatic melanoma. Melanoma Research. 23(1). 47–54. 43 indexed citations
19.
Chang, Rui & M. Stetter. (2007). A Knowledge-based Dynamic Bayesian Framework Towards Molecular Network Modeling and Quantitative Prediction.. 37–43. 2 indexed citations
20.

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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