Ru Nie

1.7k total citations · 1 hit paper
27 papers, 1.2k citations indexed

About

Ru Nie is a scholar working on Molecular Biology, Cancer Research and Artificial Intelligence. According to data from OpenAlex, Ru Nie has authored 27 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 9 papers in Cancer Research and 7 papers in Artificial Intelligence. Recurrent topics in Ru Nie's work include MicroRNA in disease regulation (9 papers), Cancer-related molecular mechanisms research (9 papers) and Machine Learning in Bioinformatics (5 papers). Ru Nie is often cited by papers focused on MicroRNA in disease regulation (9 papers), Cancer-related molecular mechanisms research (9 papers) and Machine Learning in Bioinformatics (5 papers). Ru Nie collaborates with scholars based in China, Canada and United Kingdom. Ru Nie's co-authors include Shifei Ding, Xinzheng Xu, Han Zhao, Yanan Zhang, Zhengwei Li, Zhu‐Hong You, Hongjie Jia, Jiashu Li, Wenzheng Bao and Xing Chen and has published in prestigious journals such as Bioinformatics, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Ru Nie

26 papers receiving 1.2k citations

Hit Papers

Extreme learning machine: algorithm, theory and applications 2013 2026 2017 2021 2013 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Extreme learning machine: algorithm, theory and applications
    2013 450 citations Shifei Ding, Han Zhao et al. Artificial Intelligence Review profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ru Nie China 11 484 381 208 181 142 27 1.2k
Cuili Yang China 21 429 0.9× 441 1.2× 385 1.9× 238 1.3× 33 0.2× 74 1.4k
Qian Yang China 20 375 0.8× 248 0.7× 51 0.2× 319 1.8× 171 1.2× 102 1.2k
Shengsheng Wang China 20 423 0.9× 136 0.4× 23 0.1× 94 0.5× 320 2.3× 149 1.3k
Fei Han China 21 880 1.8× 135 0.4× 25 0.1× 185 1.0× 305 2.1× 69 1.4k
Xiaohu Shi China 16 885 1.8× 132 0.3× 28 0.1× 234 1.3× 206 1.5× 64 1.7k
Olli Simula Finland 16 1.3k 2.6× 172 0.5× 77 0.4× 410 2.3× 327 2.3× 58 2.0k
Qing-Hua Ling China 16 686 1.4× 107 0.3× 18 0.1× 141 0.8× 244 1.7× 51 1.1k
Yue Chen China 15 261 0.5× 116 0.3× 94 0.5× 150 0.8× 418 2.9× 67 1.1k
Yu Zeng China 16 348 0.7× 94 0.2× 34 0.2× 125 0.7× 147 1.0× 54 948
Jianchao Zeng China 13 1.0k 2.1× 80 0.2× 72 0.3× 86 0.5× 47 0.3× 50 1.4k

Countries citing papers authored by Ru Nie

Since Specialization
Citations

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

Fields of papers citing papers by Ru Nie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ru Nie

This figure shows the co-authorship network connecting the top 25 collaborators of Ru Nie. A scholar is included among the top collaborators of Ru Nie 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 Ru Nie. Ru Nie 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.
Yang, Zhiwei, et al.. (2025). The Imitation Game revisited: A comprehensive survey on recent advances in AI-generated text detection. Expert Systems with Applications. 272. 126694–126694. 2 indexed citations
2.
Li, Zhengwei, et al.. (2024). HHOMR: a hybrid high-order moment residual model for miRNA-disease association prediction. Briefings in Bioinformatics. 25(5). 2 indexed citations
3.
Li, Zhengwei, et al.. (2024). Predicting miRNA-Disease Associations Based on Spectral Graph Transformer With Dynamic Attention and Regularization. IEEE Journal of Biomedical and Health Informatics. 28(12). 7611–7622. 2 indexed citations
4.
Li, Zhengwei, et al.. (2024). LMGATCDA: Graph Neural Network With Labeling Trick for Predicting circRNA-Disease Associations. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 21(2). 289–300. 6 indexed citations
5.
6.
Li, Zhengwei, et al.. (2023). Adversarial dense graph convolutional networks for single-cell classification. Bioinformatics. 39(2). 9 indexed citations
7.
Li, Zhengwei, et al.. (2022). Hierarchical graph attention network for miRNA-disease association prediction. Molecular Therapy. 30(4). 1775–1786. 58 indexed citations
8.
Li, Zhengwei, et al.. (2022). Predicting Mirna-Disease Associations Based on Neighbor Selection Graph Attention Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(2). 1298–1307. 15 indexed citations
9.
Nie, Ru, et al.. (2021). Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering. BMC Medical Informatics and Decision Making. 21(S1). 254–254. 1 indexed citations
10.
Li, Jiashu, et al.. (2020). FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks. Molecular Genetics and Genomics. 295(5). 1197–1209. 22 indexed citations
11.
Li, Zhengwei, et al.. (2019). Using discriminative vector machine model with 2DPCA to predict interactions among proteins. BMC Bioinformatics. 20(S25). 694–694. 5 indexed citations
12.
Li, Zhengwei, Zhu‐Hong You, Xiao Li, et al.. (2017). In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences. Scientific Reports. 7(1). 11174–11174. 70 indexed citations
13.
Li, Zhengwei, Zhu‐Hong You, Xing Chen, Jie Gui, & Ru Nie. (2016). Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics. International Journal of Molecular Sciences. 17(9). 1396–1396. 29 indexed citations
14.
Ding, Shifei, Han Zhao, Yanan Zhang, Xinzheng Xu, & Ru Nie. (2013). Extreme learning machine: algorithm, theory and applications. Artificial Intelligence Review. 44(1). 103–115. 450 indexed citations breakdown →
15.
Ding, Shifei, et al.. (2013). Twin Support Vector Machines Based on Quantum Particle Swarm Optimization. Journal of Software. 8(7). 6 indexed citations
16.
Jia, Hongjie, Shifei Ding, Xinzheng Xu, & Ru Nie. (2013). The latest research progress on spectral clustering. Neural Computing and Applications. 24(7-8). 1477–1486. 146 indexed citations
17.
Nie, Ru & Jianhua Yue. (2011). A Quantum-inspired Particle Swarm Algorithm for Inverse Problem. International Journal of Digital Content Technology and its Applications. 5(12). 272–278. 2 indexed citations
18.
Nie, Ru, et al.. (2010). A novel quantum-inspired particle swarm algorithm and its application. 2010 Sixth International Conference on Natural Computation. 2556–2560. 5 indexed citations
19.
Li, Zhengwei, Ru Nie, & Yao Han. (2010). Supervised Locally Linear Embedding for Fault Diagnosis. Advanced materials research. 139-141. 2599–2602. 2 indexed citations
20.
Nie, Ru. (2004). Research on the Metadata Standard of Data Warehousing. Microcomputer Development. 1 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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