Juan Wang

1.6k total citations
107 papers, 1.0k citations indexed

About

Juan Wang is a scholar working on Molecular Biology, Cancer Research and Computational Theory and Mathematics. According to data from OpenAlex, Juan Wang has authored 107 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 72 papers in Molecular Biology, 15 papers in Cancer Research and 12 papers in Computational Theory and Mathematics. Recurrent topics in Juan Wang's work include Gene expression and cancer classification (34 papers), Bioinformatics and Genomic Networks (32 papers) and Machine Learning in Bioinformatics (17 papers). Juan Wang is often cited by papers focused on Gene expression and cancer classification (34 papers), Bioinformatics and Genomic Networks (32 papers) and Machine Learning in Bioinformatics (17 papers). Juan Wang collaborates with scholars based in China, United States and Hong Kong. Juan Wang's co-authors include Jin‐Xing Liu, Ying-Lian Gao, Chun-Hou Zheng, Junliang Shang, Ling-Yun Dai, Shasha Yuan, Feifei Tan, Xiangdong Li, Zhen Cui and Xiang-Zhen Kong and has published in prestigious journals such as Journal of Biological Chemistry, Scientific Reports and Chemical Engineering Journal.

In The Last Decade

Juan Wang

96 papers receiving 998 citations

Peers

Juan Wang

GENET

Kenji Satou Japan

Yanli Zou China

Dhammika Amaratunga United States

Jennifer R. Smith United States

Mike Tyka United States

Mark P. Styczynski United States

Kyungsook Han South Korea

Joseph Mellor United States

Peng Ni China

Xiaogen Zhou China

Kenji Satou Japan

Juan Wang

659 ×1.2

199 ×1.3

122 ×1.1

135 ×1.5

50 ×0.6

GENET

Citations per year, relative to Juan Wang Juan Wang (= 1×) peers Kenji Satou

Countries citing papers authored by Juan Wang

Since Specialization

Citations

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

Fields of papers citing papers by Juan Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Juan Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Juan Wang. A scholar is included among the top collaborators of Juan 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 Juan Wang. Juan Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Liu, Jin‐Xing, et al.. (2024). M³HOGAT: A Multi-View Multi-Modal Multi-Scale High-Order Graph Attention Network for Microbe-Disease Association Prediction. IEEE Journal of Biomedical and Health Informatics. 28(10). 6259–6267. 2 indexed citations

Wang, Pei, Juan Wang, Yun Zhou, et al.. (2024). miR-107 reverses the multidrug resistance of gastric cancer by targeting the CGA/EGFR/GATA2 positive feedback circuit. Journal of Biological Chemistry. 300(8). 107522–107522. 3 indexed citations

Wang, Juan, et al.. (2024). A New Graph Autoencoder-Based Multi-Level Kernel Subspace Fusion Framework for Single-Cell Type Identification. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 21(6). 2292–2303.

Yuan, Shasha, K.Q. Yan, Shihan Wang, Jin‐Xing Liu, & Juan Wang. (2024). EEG-Based Seizure Prediction Using Hybrid DenseNet–ViT Network with Attention Fusion. Brain Sciences. 14(8). 839–839. 10 indexed citations

Feng, Ying, Aswathi Soni, Gale Brightwell, et al.. (2024). The potential new microbial hazard monitoring tool in food safety: Integration of metabolomics and artificial intelligence. Trends in Food Science & Technology. 149. 104555–104555. 17 indexed citations

Wang, Linping, et al.. (2023). KGLRR: A low-rank representation K-means with graph regularization constraint method for Single-cell type identification. Computational Biology and Chemistry. 104. 107862–107862. 1 indexed citations

Wang, Ying, Jin‐Xing Liu, Juan Wang, Junliang Shang, & Ying-Lian Gao. (2023). A Graph Representation Approach Based on Light Gradient Boosting Machine for Predicting Drug–Disease Associations. Journal of Computational Biology. 30(8). 937–947. 1 indexed citations

Gao, Ying-Lian, Qian Qiao, Juan Wang, Shasha Yuan, & Jin‐Xing Liu. (2023). BioSTD: A New Tensor Multi-View Framework via Combining Tensor Decomposition and Strong Complementarity Constraint for Analyzing Cancer Omics Data. IEEE Journal of Biomedical and Health Informatics. 27(10). 5187–5198. 5 indexed citations

Wang, Ying, Ying-Lian Gao, Juan Wang, Feng Li, & Jin‐Xing Liu. (2023). MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities Graph Convolutional Autoencoder. IEEE Journal of Biomedical and Health Informatics. 27(7). 3686–3694. 10 indexed citations

10.

Liu, Fang, et al.. (2023). A Graph Neural Network Recommendation Method Integrating Multi Head Attention Mechanism and Improved Gated Recurrent Unit Algorithm. IEEE Access. 11. 116879–116891. 1 indexed citations

11.

Li, Feng, et al.. (2023). scFED: Clustering Identifying Cell Types of scRNA-Seq Data Based on Feature Engineering Denoising. Interdisciplinary Sciences Computational Life Sciences. 15(4). 590–601. 1 indexed citations

12.

Yuan, Shasha, Jianwei Mu, Weidong Zhou, et al.. (2022). Automatic Epileptic Seizure Detection Using Graph-Regularized Non-Negative Matrix Factorization and Kernel-Based Robust Probabilistic Collaborative Representation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 30. 2641–2650. 9 indexed citations

13.

Gao, Ying-Lian, et al.. (2022). A new framework for drug–disease association prediction combing light-gated message passing neural network and gated fusion mechanism. Briefings in Bioinformatics. 23(6). 18 indexed citations

14.

Wang, Juan, et al.. (2022). Multi-View Random-Walk Graph Regularization Low-Rank Representation for Cancer Clustering and Differentially Expressed Gene Selection. IEEE Journal of Biomedical and Health Informatics. 26(7). 3578–3589. 5 indexed citations

15.

Chen, Shuo, et al.. (2021). LncRNA AGAP2-AS1 Promotes Cancer Cell Proliferation, Migration and Invasion in Colon Cancer by Forming a Negative Feedback Loop with LINC-PINT. Cancer Management and Research. Volume 13. 2153–2161. 8 indexed citations

16.

Dai, Ling-Yun, Rong Zhu, & Juan Wang. (2020). Joint Nonnegative Matrix Factorization Based on Sparse and Graph Laplacian Regularization for Clustering and Co-Differential Expression Genes Analysis. Complexity. 2020. 1–10. 4 indexed citations

17.

Yamada, Ryo, et al.. (2020). Interpretation of omics data analyses. Journal of Human Genetics. 66(1). 93–102. 52 indexed citations

18.

Gao, Ying-Lian, et al.. (2016). A graph-Laplacian PCA based on L1/2-norm constraint for characteristic gene selection. IEEE Conference Proceedings. 2016. 1799. 3 indexed citations

19.

Zhao, Huihui, Na Hou, Jianxin Chen, et al.. (2011). Identifying Metabolite and Protein Biomarkers in Unstable Angina In-patients by Feature Selection Based Data Mining Method. Chemical Research in Chinese Universities. 27(1). 87–93. 10 indexed citations

20.

Yang, Jingsong, et al.. (2008). Data fusion of significant wave height from multiple satellite altimeters. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7154. 715408–715408. 6 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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