Wei Wang

38.4k total citations · 2 hit papers
857 papers, 20.3k citations indexed

About

Wei Wang is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Wei Wang has authored 857 papers receiving a total of 20.3k indexed citations (citations by other indexed papers that have themselves been cited), including 263 papers in Artificial Intelligence, 232 papers in Molecular Biology and 153 papers in Information Systems. Recurrent topics in Wei Wang's work include Data Management and Algorithms (101 papers), Data Mining Algorithms and Applications (73 papers) and Bioinformatics and Genomic Networks (72 papers). Wei Wang is often cited by papers focused on Data Management and Algorithms (101 papers), Data Mining Algorithms and Applications (73 papers) and Bioinformatics and Genomic Networks (72 papers). Wei Wang collaborates with scholars based in China, United States and Australia. Wei Wang's co-authors include Jiong Yang, Philip S. Yu, Xuemin Lin, Richard R. Muntz, Haixun Wang, Jan F. Prins, Chuan Xiao, Jun Huan, Jiong Yang and Jeffrey Xu Yu and has published in prestigious journals such as Science, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Wei Wang

786 papers receiving 19.5k citations

Hit Papers

STING: A Statistical Info... 1997 2026 2006 2016 1997 2018 250 500 750

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Wei Wang 6.3k 6.0k 3.9k 3.4k 2.7k 857 20.3k
David Heckerman 7.9k 1.3× 3.3k 0.5× 2.6k 0.7× 1.4k 0.4× 1.1k 0.4× 219 18.1k
Nello Cristianini 11.2k 1.8× 6.7k 1.1× 1.8k 0.4× 2.3k 0.7× 983 0.4× 288 31.9k
Martin Ester 10.6k 1.7× 3.8k 0.6× 6.4k 1.6× 4.9k 1.4× 2.8k 1.1× 175 26.7k
Daphne Koller 11.8k 1.9× 8.5k 1.4× 1.9k 0.5× 1.4k 0.4× 1.5k 0.6× 216 30.6k
Eibe Frank 18.3k 2.9× 4.8k 0.8× 9.1k 2.3× 3.9k 1.1× 3.7k 1.4× 102 38.5k
Nir Friedman 7.8k 1.2× 17.5k 2.9× 1.4k 0.4× 1.2k 0.3× 961 0.4× 210 30.5k
Charles Elkan 5.7k 0.9× 4.9k 0.8× 1.6k 0.4× 1.1k 0.3× 1.1k 0.4× 97 14.6k
John Shawe‐Taylor 16.3k 2.6× 3.9k 0.6× 2.1k 0.5× 3.6k 1.1× 2.3k 0.9× 312 36.7k
Mark Hall 7.8k 1.2× 2.8k 0.5× 3.5k 0.9× 2.1k 0.6× 2.0k 0.7× 88 18.8k
Thomas Hofmann 9.5k 1.5× 8.2k 1.4× 4.0k 1.0× 1.5k 0.4× 868 0.3× 764 42.2k

Countries citing papers authored by Wei Wang

Since Specialization
Citations

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

Fields of papers citing papers by Wei Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wei Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Wei Wang. A scholar is included among the top collaborators of Wei 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 Wei Wang. Wei 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.
Zhao, Yi, et al.. (2025). FOCUS-DWI improves prostate cancer detection through deep learning reconstruction with IQMR technology. Abdominal Radiology. 51(3). 1276–1288.
2.
Song, Hailiang, et al.. (2025). A Deep Autoencoder Compression-Based Genomic Prediction Method for Whole-Genome Sequencing Data. Biology. 14(11). 1622–1622.
3.
Wang, Wei, Yuying Deng, Bin Hou, et al.. (2025). Bifunctional chitosan/tannin aerogel for gold recovery via electrostatic attraction and in-situ reduction. Journal of Hazardous Materials. 490. 137839–137839. 7 indexed citations
5.
Liao, Junyun, et al.. (2024). Perceived identity threat and brand advocacy responses to different types of brand-related attacks. Internet Research. 35(3). 1274–1298. 2 indexed citations
6.
Cui, Pengfei, et al.. (2024). The Research of Ultra-Low Delay Gateway for Under-ground Remote Control. Radioengineering. 33(3). 452–462.
7.
Snyder, Kristen, et al.. (2024). sendigR: an R package to leverage the value of CDISC SEND datasets for cross-study analysis. SHILAP Revista de lepidopterología. 6. 1392686–1392686. 1 indexed citations
8.
Chen, Yangwu, Rui Wang, Houzhen Zhou, et al.. (2024). Enhancing long-term river water quality prediction: Construction and validation of an improved hybrid model. Process Safety and Environmental Protection. 186. 388–398. 3 indexed citations
9.
Song, Chao, et al.. (2024). DUSP6 protein action and related hub genes prevention of sepsis-induced lung injury were screened by WGCNA and Venn. International Journal of Biological Macromolecules. 279(Pt 1). 135117–135117. 1 indexed citations
10.
Shi, Jun, Dongdong Sun, Zhiguo Jiang, et al.. (2024). Positional encoding-guided transformer-based multiple instance learning for histopathology whole slide images classification. Computer Methods and Programs in Biomedicine. 258. 108491–108491. 6 indexed citations
11.
Jiang, Feifeng, Jun Ma, Chris Webster, Wei Wang, & Jack C.P. Cheng. (2024). Automated site planning using CAIN-GAN model. Automation in Construction. 159. 105286–105286. 20 indexed citations
12.
Li, Xinyue, Meina Quan, Yiping Wei, et al.. (2023). Critical thinking of Alzheimer’s transgenic mouse model: current research and future perspective. Science China Life Sciences. 66(12). 2711–2754. 8 indexed citations
13.
Jiang, Jyun‐Yu, Chelsea J.‐T. Ju, Junheng Hao, Muhao Chen, & Wei Wang. (2021). JEDI: circular RNA prediction based on junction encoders and deep interaction among splice sites. Bioinformatics. 37(Supplement_1). i289–i298. 15 indexed citations
14.
Zhou, Guangyu, Muhao Chen, Chelsea J.‐T. Ju, et al.. (2020). Mutation effect estimation on protein–protein interactions using deep contextualized representation learning. NAR Genomics and Bioinformatics. 2(2). lqaa015–lqaa015. 39 indexed citations
15.
Chen, Muhao, Chelsea J.‐T. Ju, Guangyu Zhou, et al.. (2019). Multifaceted protein–protein interaction prediction based on Siamese residual RCNN. Bioinformatics. 35(14). i305–i314. 216 indexed citations
16.
Setty, Shaun P., David A. Liem, Yu Shi, et al.. (2019). Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications. Journal of Visualized Experiments. 3 indexed citations
17.
Setty, Shaun P., David A. Liem, Yu Shi, et al.. (2019). Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications. Journal of Visualized Experiments. 4 indexed citations
18.
Liu, Wei, Qiuyu Wang, Jianmei Zhao, et al.. (2015). Integration of pathway structure information into a reweighted partial Cox regression approach for survival analysis on high-dimensional gene expression data. Molecular BioSystems. 11(7). 1876–1886. 4 indexed citations
19.
Bonafé, Luisa, Jinlong Liang, Maria W. Górna, et al.. (2014). MMP13 mutations are the cause of recessive metaphyseal dysplasia, Spahr type. American Journal of Medical Genetics Part A. 164(5). 1175–1179. 12 indexed citations
20.
Wang, Wei, R. T. BORCHARDT, & Binghe Wang. (2000). Orally Active Peptidomimetic RGD Analogs that are Glycoprotein IIb/IIIa Antagonists. Current Medicinal Chemistry. 7(4). 437–453. 29 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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