Le Ou-Yang

1.7k total citations
95 papers, 1.1k citations indexed

About

Le Ou-Yang is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cancer Research. According to data from OpenAlex, Le Ou-Yang has authored 95 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Molecular Biology, 19 papers in Computational Theory and Mathematics and 9 papers in Cancer Research. Recurrent topics in Le Ou-Yang's work include Bioinformatics and Genomic Networks (51 papers), Gene expression and cancer classification (37 papers) and Gene Regulatory Network Analysis (21 papers). Le Ou-Yang is often cited by papers focused on Bioinformatics and Genomic Networks (51 papers), Gene expression and cancer classification (37 papers) and Gene Regulatory Network Analysis (21 papers). Le Ou-Yang collaborates with scholars based in China, Hong Kong and Singapore. Le Ou-Yang's co-authors include Xiao-Fei Zhang, Hong Yan, Dao‐Qing Dai, Min Wu, Xiaoli Li, Xing‐Ming Zhao, Xiaohua Hu, Mengyun Wu, Yuan Zhu and Zexuan Zhu and has published in prestigious journals such as Nature Biotechnology, Bioinformatics and PLoS ONE.

In The Last Decade

Le Ou-Yang

87 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Le Ou-Yang China	20	840	231	108	102	52	95	1.1k
Rui Kuang United States	24	987 1.2×	148 0.6×	249 2.3×	115 1.1×	46 0.9×	74	1.6k
Ruiqing Zheng China	20	870 1.0×	228 1.0×	102 0.9×	175 1.7×	24 0.5×	58	1.2k
Noël Malod‐Dognin United Kingdom	14	543 0.6×	189 0.8×	121 1.1×	30 0.3×	144 2.8×	31	812
Vladimir Gligorijević United States	14	937 1.1×	246 1.1×	111 1.0×	52 0.5×	86 1.7×	24	1.3k
Charalampos Moschopoulos Greece	8	377 0.4×	113 0.5×	80 0.7×	24 0.2×	74 1.4×	15	692
Fabrizio Costa Germany	18	723 0.9×	77 0.3×	244 2.3×	102 1.0×	33 0.6×	45	1.0k
Juan Wang China	18	540 0.6×	70 0.3×	91 0.8×	156 1.5×	8 0.2×	107	1.0k
Hyunghoon Cho United States	13	488 0.6×	131 0.6×	345 3.2×	102 1.0×	12 0.2×	30	927
Changiz Eslahchi Iran	16	586 0.7×	369 1.6×	102 0.9×	43 0.4×	9 0.2×	81	910
Falk Schreiber Germany	16	725 0.9×	95 0.4×	51 0.5×	23 0.2×	133 2.6×	34	1.1k

Countries citing papers authored by Le Ou-Yang

Since Specialization

Citations

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

Fields of papers citing papers by Le Ou-Yang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Le Ou-Yang

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Yu, Weiming, et al.. (2025). Structure-enhanced graph meta learning for few-shot gene regulatory network inference. Genome biology. 26(1). 394–394.

Wang, Yi, et al.. (2025). LGFFM: A Localized and Globalized Frequency Fusion Model for Ultrasound Image Segmentation. IEEE Transactions on Medical Imaging. 45(2). 515–527.

Li, Ying, et al.. (2025). Unveiling spatial domains from spatial multi-omics data using dual-graph regularized ensemble learning. Communications Biology. 8(1). 945–945. 2 indexed citations

Cai, Guangyan, et al.. (2025). SpaFusion: A multi-level fusion model for clustering spatial multi-omics data. Information Fusion. 124. 103372–103372.

Wu, Yongxian, et al.. (2024). Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning. Bioinformatics. 40(4). 8 indexed citations

Zhao, Kangfei, et al.. (2024). Equivariant Line Graph Neural Network for Protein-Ligand Binding Affinity Prediction. IEEE Journal of Biomedical and Health Informatics. 28(7). 4336–4347. 2 indexed citations

Chen, Yaowen, Guohua Dong, Le Ou-Yang, et al.. (2024). Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS. Nature Biotechnology. 42(10). 1594–1605. 32 indexed citations

Chen, Yujie, et al.. (2024). GRESS: Grouping Belief-Based Deep Contrastive Subspace Clustering. IEEE Transactions on Cybernetics. 55(1). 148–160. 3 indexed citations

Liu, Jiahan, et al.. (2023). A partially shared joint clustering framework for detecting protein complexes from multiple state-specific signed interaction networks. Computers in Biology and Medicine. 159. 106936–106936. 2 indexed citations

10.

Ou-Yang, Le, et al.. (2020). LRSK: a low-rank self-representation K -means method for clustering single-cell RNA-sequencing data. Molecular Omics. 16(5). 465–473. 7 indexed citations

11.

Tan, Ee-Leng, Peng Yang, Shan Huang, et al.. (2020). Self-weighted adaptive structure learning for ASD diagnosis via multi-template multi-center representation. Medical Image Analysis. 63. 101662–101662. 29 indexed citations

12.

Zhang, Dexin, et al.. (2020). EC-PGMGR: Ensemble Clustering Based on Probability Graphical Model With Graph Regularization for Single-Cell RNA-seq Data. Frontiers in Genetics. 11. 572242–572242. 6 indexed citations

13.

Ou-Yang, Le, Xiao-Fei Zhang, Yan-Ran Li, et al.. (2019). LncRNA-Disease Association Prediction Using Two-Side Sparse Self-Representation. Frontiers in Genetics. 10. 476–476. 12 indexed citations

14.

Zhang, Jiaxin, et al.. (2018). Performance Evaluation of Double-edge Satellite Terrestrial Networks on OPNET Platform. 37–42. 6 indexed citations

15.

Zhang, Xiao-Fei, Le Ou-Yang, & Hong Yan. (2017). Node-based differential network analysis in genomics. Computational Biology and Chemistry. 69. 194–201. 10 indexed citations

16.

Ou-Yang, Le, Hong Yan, & Xiao-Fei Zhang. (2016). Identifying differential networks based on multi-platform gene expression data. Molecular BioSystems. 13(1). 183–192. 12 indexed citations

17.

Zhang, Xiao-Fei, Le Ou-Yang, Dao‐Qing Dai, et al.. (2016). Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks. BMC Bioinformatics. 17(1). 358–358. 12 indexed citations

18.

Wang, Liya, Le Ou-Yang, Fei Tong, et al.. (2016). The effect of contraceptive methods on reproductive tract infections risk: a cross-sectional study having a sample of 52,481 women. Archives of Gynecology and Obstetrics. 294(6). 1249–1256. 11 indexed citations

19.

Zhang, Xiaofei, Le Ou-Yang, Xiaohua Hu, & Dao‐Qing Dai. (2015). Identifying binary protein-protein interactions from affinity purification mass spectrometry data. BMC Genomics. 16(1). 745–745. 13 indexed citations

20.

Zhang, Xiao-Fei, Dao‐Qing Dai, Le Ou-Yang, & Hong Yan. (2014). Detecting overlapping protein complexes based on a generative model with functional and topological properties. BMC Bioinformatics. 15(1). 186–186. 35 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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