Mengyun Yang

1.8k total citations
34 papers, 1.2k citations indexed

About

Mengyun Yang is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Mengyun Yang has authored 34 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 18 papers in Computational Theory and Mathematics and 7 papers in Materials Chemistry. Recurrent topics in Mengyun Yang's work include Computational Drug Discovery Methods (18 papers), Bioinformatics and Genomic Networks (9 papers) and Machine Learning in Bioinformatics (6 papers). Mengyun Yang is often cited by papers focused on Computational Drug Discovery Methods (18 papers), Bioinformatics and Genomic Networks (9 papers) and Machine Learning in Bioinformatics (6 papers). Mengyun Yang collaborates with scholars based in China, United States and Canada. Mengyun Yang's co-authors include Jianxin Wang, Yaohang Li, Fang‐Xiang Wu, Huimin Luo, Min Li, Qichang Zhao, Chengqian Lu, Feng Luo, Yi Pan and Min Li and has published in prestigious journals such as Environmental Science & Technology, Bioinformatics and Environmental Pollution.

In The Last Decade

Mengyun Yang

32 papers receiving 1.2k citations

Peers

Mengyun Yang

CTM

Joerg Wichard Germany

Angela Serra Finland

Glenn J. Myatt United States

Darren T. Andrews United Kingdom

Cynthia Saraceni-Richards United States

Fredrik Svensson United Kingdom

Alex Aliper United States

Jeroen Kazius Netherlands

Leihong Wu United States

Joerg Wichard Germany

Mengyun Yang

390 ×0.5

481 ×0.9

CTM

88 ×0.3

186 ×1.4

112 ×1.3

Citations per year, relative to Mengyun Yang Mengyun Yang (= 1×) peers Joerg Wichard

Countries citing papers authored by Mengyun Yang

Since Specialization

Citations

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

Fields of papers citing papers by Mengyun Yang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mengyun Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Mengyun Yang. A scholar is included among the top collaborators of Mengyun Yang 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 Mengyun Yang. Mengyun Yang 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

Yang, Mengyun, Bin Yang, Jiajun Chen, Xiwei Tang, & Guihua Duan. (2025). A Novel Drug–Disease Association Prediction Method Based on Deep Non-Negative Matrix Factorization with Local Graph Feature. Interdisciplinary Sciences Computational Life Sciences. 17(3). 598–613.

Yao, Jun, et al.. (2025). Synergistic catalysis of Fe-Co bimetallic sulfides for efficient water splitting. Applied Surface Science. 696. 162901–162901. 5 indexed citations

Tang, Xiwei, et al.. (2024). MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction. Methods. 231. 1–7. 3 indexed citations

Tang, Xiwei, Yiqiang Zhou, Mengyun Yang, & Wenjun Li. (2024). TC-DTA: Predicting Drug-Target Binding Affinity With Transformer and Convolutional Neural Networks. IEEE Transactions on NanoBioscience. 23(4). 572–578. 5 indexed citations

Zheng, Kai, Guihua Duan, Qichang Zhao, et al.. (2024). DLP: Duplex Link Prediction Via Subspace Segmentation for Predicting drug-MiRNA Associations. PubMed. PP. 1–9. 2 indexed citations

Li, Wenjun, et al.. (2024). MFCM-DTI model of multimodal feature fusion: prediction of drug-target interaction. 687–692.

Yang, Mengyun, et al.. (2024). Drug repurposing based on the DTD-GNN graph neural network: revealing the relationships among drugs, targets and diseases. BMC Genomics. 25(1). 584–584. 9 indexed citations

Yang, Mengyun, Bin Yang, Guihua Duan, & Jianxin Wang. (2023). ITRPCA: a new model for computational drug repositioning based on improved tensor robust principal component analysis. Frontiers in Genetics. 14. 1271311–1271311. 3 indexed citations

Liang, Xiaoshan, Xiaoxiang Wang, Mengyun Yang, et al.. (2023). α-Fe2O3-supported Co3O4 nanoparticles to construct highly active interfacial oxygen vacancies for ozone decomposition. Environmental Pollution. 330. 121704–121704. 20 indexed citations

10.

Zhao, Qichang, Guihua Duan, Mengyun Yang, et al.. (2022). AttentionDTA: Drug–Target Binding Affinity Prediction by Sequence-Based Deep Learning With Attention Mechanism. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(2). 852–863. 55 indexed citations

11.

Zhao, Qichang, Mengyun Yang, Zhongjian Cheng, Yaohang Li, & Jianxin Wang. (2021). Biomedical Data and Deep Learning Computational Models for Predicting Compound-Protein Relations. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 19(4). 2092–2110. 24 indexed citations

12.

Yang, Mengyun, et al.. (2021). De Novo Prediction of Drug–Target Interactions Using Laplacian Regularized Schatten p -Norm Minimization. Journal of Computational Biology. 28(7). 660–673. 3 indexed citations

13.

Li, Hong‐Dong, Zhimin Zhang, Mengyun Yang, et al.. (2020). IsoResolve: predicting splice isoform functions by integrating gene and isoform-level features with domain adaptation. Bioinformatics. 37(4). 522–530. 9 indexed citations

14.

Yang, Mengyun, Yaohang Li, & Jianxin Wang. (2020). Feature and Nuclear Norm Minimization for Matrix Completion. IEEE Transactions on Knowledge and Data Engineering. 34(5). 2190–2199. 16 indexed citations

15.

Yang, Mengyun, et al.. (2020). miRTMC: A miRNA Target Prediction Method Based on Matrix Completion Algorithm. IEEE Journal of Biomedical and Health Informatics. 24(12). 3630–3641. 9 indexed citations

16.

Yang, Mengyun, Huimin Luo, Yaohang Li, Fang‐Xiang Wu, & Jianxin Wang. (2019). Overlap matrix completion for predicting drug-associated indications. PLoS Computational Biology. 15(12). e1007541–e1007541. 38 indexed citations

17.

Yang, Mengyun, Huimin Luo, Yaohang Li, & Jianxin Wang. (2019). Drug repositioning based on bounded nuclear norm regularization. Bioinformatics. 35(14). i455–i463. 125 indexed citations

18.

Wang, Hua, Zhihao Wang, Jing Kong, et al.. (2011). Oxidized Low-Density Lipoprotein-Dependent Platelet-Derived Microvesicles Trigger Procoagulant Effects and Amplify Oxidative Stress. Molecular Medicine. 18(2). 159–166. 47 indexed citations

19.

Yang, Mengyun. (2010). Origin and distribution of interbeds and the influence on oil-water layer:An example from Mosuowan area in the hinterland of Junggar Basin. Lithologic Reservoirs. 3 indexed citations

20.

Jones, M. Z., Mengyun Yang, & Olaf Mickelsen. (1972). Effects of methylazoxymethanol glucoside and methylazoxymethanol acetate on the cerebellum of the postnatal Swiss albino mouse.. PubMed. 31(5). 1508–11. 26 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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