Edwin Wang

4.9k total citations
87 papers, 3.2k citations indexed

About

Edwin Wang is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Edwin Wang has authored 87 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Molecular Biology, 22 papers in Cancer Research and 12 papers in Oncology. Recurrent topics in Edwin Wang's work include Bioinformatics and Genomic Networks (19 papers), Cancer Genomics and Diagnostics (14 papers) and Gene expression and cancer classification (11 papers). Edwin Wang is often cited by papers focused on Bioinformatics and Genomic Networks (19 papers), Cancer Genomics and Diagnostics (14 papers) and Gene expression and cancer classification (11 papers). Edwin Wang collaborates with scholars based in Canada, United States and China. Edwin Wang's co-authors include Enrico O. Purisima, Qinghua Cui, Jinfeng Zou, Maureen D. O'Connor‐McCourt, Zhenbao Yu, Chabane Tibiche, Mark Trifiro, Naif Zaman, Shi‐Hsiang Shen and Catherine Collins and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Journal of Clinical Investigation.

In The Last Decade

Edwin Wang

83 papers receiving 3.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Edwin Wang Canada 31 2.1k 936 445 293 271 87 3.2k
Diana Lee United States 20 2.7k 1.3× 673 0.7× 278 0.6× 206 0.7× 539 2.0× 39 3.8k
Lambert C. J. Dorssers Netherlands 36 2.1k 1.0× 904 1.0× 193 0.4× 719 2.5× 285 1.1× 91 3.5k
Peter M. Haverty United States 29 2.4k 1.2× 677 0.7× 105 0.2× 389 1.3× 351 1.3× 38 3.3k
Oussema Souiai Tunisia 7 1.7k 0.8× 508 0.5× 125 0.3× 217 0.7× 186 0.7× 10 2.6k
Svitlana Tyekucheva United States 28 1.6k 0.8× 878 0.9× 185 0.4× 560 1.9× 542 2.0× 88 2.8k
Sasha A. Singh United States 24 1.4k 0.7× 235 0.3× 331 0.7× 183 0.6× 244 0.9× 83 2.5k
Raj Chari United States 36 3.2k 1.5× 816 0.9× 299 0.7× 541 1.8× 473 1.7× 98 4.2k
Mariano J. Alvarez United States 27 2.8k 1.3× 771 0.8× 201 0.5× 185 0.6× 291 1.1× 47 4.1k
Thierry Dubois France 32 2.7k 1.3× 659 0.7× 124 0.3× 286 1.0× 293 1.1× 79 3.8k
Zhihua Li China 33 3.0k 1.4× 1.3k 1.4× 260 0.6× 264 0.9× 197 0.7× 123 3.7k

Countries citing papers authored by Edwin Wang

Since Specialization
Citations

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

Fields of papers citing papers by Edwin Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Edwin Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Edwin Wang. A scholar is included among the top collaborators of Edwin 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 Edwin Wang. Edwin 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.
Zheng, Ruiqing, et al.. (2025). A Flexible Data-Driven Framework for Correcting Coarsely Annotated scRNA-seq Data. Big Data Mining and Analytics. 8(5). 997–1010.
2.
Bonni, Shirin, David N. Brindley, M. Dean Chamberlain, et al.. (2024). Breast Tumor Metastasis and Its Microenvironment: It Takes Both Seed and Soil to Grow a Tumor and Target It for Treatment. Cancers. 16(5). 911–911. 4 indexed citations
3.
Lu, Chao, et al.. (2024). Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects. International Journal of Molecular Sciences. 25(3). 1479–1479. 3 indexed citations
4.
Zheng, Ruiqing, Ziwei Xu, Yanping Zeng, Edwin Wang, & Min Li. (2023). SPIDE: A single cell potency inference method based on the local cell-specific network entropy. Methods. 220. 90–97. 1 indexed citations
5.
Wang, Jianxin, Min Li, Edwin Wang, Jing Tang, & Bin Hu. (2023). The Impact of Computational Drug Discovery on Society. IEEE Transactions on Computational Social Systems. 10(5). 2148–2159.
6.
Li, Jie, Xin Li, John Hutchinson, et al.. (2022). An ensemble prediction model for COVID-19 mortality risk. Biology Methods and Protocols. 7(1). bpac029–bpac029.
7.
Pader, Joy, Dylan E. O’Sullivan, Yibing Ruan, et al.. (2021). Examining the etiology of early-onset breast cancer in the Canadian Partnership for Tomorrow’s Health (CanPath). Cancer Causes & Control. 32(10). 1117–1128. 6 indexed citations
8.
Tibiche, Chabane, Naif Zaman, Jinfeng Zou, et al.. (2021). eTumorMetastasis: A Network-Based Algorithm Predicts Clinical Outcomes Using Whole-Exome Sequencing Data of Cancer Patients. Genomics Proteomics & Bioinformatics. 19(6). 973–985. 6 indexed citations
9.
Wang, Edwin, et al.. (2021). The relationship between mast cell activation syndrome, postural tachycardia syndrome, and Ehlers-Danlos syndrome. Allergy and Asthma Proceedings. 42(3). 243–246. 32 indexed citations
10.
Xu, Xue, Yuan Zhou, Xiaowen Feng, et al.. (2020). Germline genomic patterns are associated with cancer risk, oncogenic pathways, and clinical outcomes. Science Advances. 6(48). 13 indexed citations
11.
Wang, Edwin, et al.. (2019). Germline Mutations and Their Clinical Applications in Cancer. SHILAP Revista de lepidopterología. 8(1). 5 indexed citations
12.
Hao, Tong, et al.. (2019). The Protein–Protein Interaction Network of Litopenaeus vannamei Haemocytes. Frontiers in Physiology. 10. 156–156. 9 indexed citations
13.
Paliouras, Miltiadis, Lenore K. Beitel, Bruce Gottlieb, et al.. (2015). Characterization of the NPC1L1 gene and proteome from an exceptional responder to ezetimibe. Atherosclerosis. 246. 78–86. 5 indexed citations
14.
Ren, Maozhi, Prakash Venglat, Shuqing Qiu, et al.. (2012). Target of Rapamycin Signaling Regulates Metabolism, Growth, and Life Span in Arabidopsis   . The Plant Cell. 24(12). 4850–4874. 198 indexed citations
15.
Qiu, Chengxiang, Dong Wang, Edwin Wang, & Qinghua Cui. (2012). An upstream interacting context based framework for the computational inference of microRNA functions. Molecular BioSystems. 8(5). 1492–1498. 9 indexed citations
16.
Wang, Edwin. (2012). Understanding genomic alterations in cancer genomes using an integrative network approach. Cancer Letters. 340(2). 261–269. 55 indexed citations
17.
Li, Jie, et al.. (2009). Signaling network analysis of ubiquitin-mediated proteins suggests correlations between the 26S proteasome and tumor progression. Molecular BioSystems. 5(12). 1809–1816. 31 indexed citations
18.
Zoumalan, Richard A., et al.. (2008). Flat Panel Cone Beam Computed Tomography of the Sinuses. Otolaryngology. 139(S2). 1 indexed citations
19.
Wang, Edwin, et al.. (2001). Rosai-Dorfman disease presenting with isolated bilateral orbital masses: report of two cases.. American Journal of Neuroradiology. 22(7). 1386–8. 48 indexed citations
20.
Hata, Cary, Edwin Wang, Yasuharu Noishiki, et al.. (1992). Evaluation of a Polyepoxy Compound Fixed Biological Vascular Prosthesis and an Expanded Polytetrafluoroethylene Vascular Graft. Artificial Organs. 16(3). 263–266. 28 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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