Yunan Luo

2.9k total citations · 2 hit papers
31 papers, 1.7k citations indexed

About

Yunan Luo is a scholar working on Molecular Biology, Computational Theory and Mathematics and Environmental Engineering. According to data from OpenAlex, Yunan Luo has authored 31 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 12 papers in Computational Theory and Mathematics and 5 papers in Environmental Engineering. Recurrent topics in Yunan Luo's work include Computational Drug Discovery Methods (11 papers), Protein Structure and Dynamics (8 papers) and Machine Learning in Bioinformatics (6 papers). Yunan Luo is often cited by papers focused on Computational Drug Discovery Methods (11 papers), Protein Structure and Dynamics (8 papers) and Machine Learning in Bioinformatics (6 papers). Yunan Luo collaborates with scholars based in United States, China and Netherlands. Yunan Luo's co-authors include Jian Peng, Jianyang Zeng, Wenhua Kuang, Yanqing Zhang, Ligong Chen, Xinbin Zhao, Jinglin Yang, Jingtian Zhou, Tianhao Yu and Guangde Jiang and has published in prestigious journals such as Science, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Yunan Luo

27 papers receiving 1.6k citations

Hit Papers

A network integration approach for drug-target interactio... 2017 2026 2020 2023 2017 2023 200 400 600
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. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
    2017 635 citations Yunan Luo, Xinbin Zhao et al. Nature Communications profile →
  2. Enzyme function prediction using contrastive learning
    2023 236 citations Tianhao Yu, Haiyang Cui et al. Science profile →

Peers

Yunan Luo
Ming Wen China
Xiuzhen Huang United States
Günter Klambauer Austria
Jie Zheng China
Shao‐Wu Zhang China
Jun Zou China
Lifeng Peng New Zealand
Friedrich Reinhard Germany
Chao Shen China
Ming Wen China
Yunan Luo
Citations per year, relative to Yunan Luo Yunan Luo (= 1×) peers Ming Wen

Countries citing papers authored by Yunan Luo

Since Specialization
Citations

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

Fields of papers citing papers by Yunan Luo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yunan Luo

This figure shows the co-authorship network connecting the top 25 collaborators of Yunan Luo. A scholar is included among the top collaborators of Yunan Luo 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 Yunan Luo. Yunan Luo 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.
Zhang, Peiwen, et al.. (2025). Air transportation carbon dioxide emission forecasting: An improved back propagation neural network. PLoS ONE. 20(10). e0333226–e0333226.
2.
Luo, Yunan & Jiaxin Cai. (2025). Deep Learning for the Prediction of Protein Sequence, Structure, Function, and Interaction: Applications, Challenges, and Future Directions. Current Proteomics. 21(6). 561–579. 1 indexed citations
3.
Luo, Jiaqi, et al.. (2025). Pareto-optimal sampling for multi-objective protein sequence design. iScience. 28(3). 112119–112119. 1 indexed citations
4.
Luo, Yunan, et al.. (2025). Generalizable and scalable protein stability prediction with rewired protein generative models. Nature Communications. 17(1). 891–891.
5.
Luo, Yunan, et al.. (2024). BioDolphin as a comprehensive database of lipid–protein binding interactions. Communications Chemistry. 7(1). 288–288.
6.
Chin, M. A., Wei Huang, Binh Khanh, et al.. (2024). Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering. Nature Communications. 15(1). 6392–6392. 27 indexed citations
7.
Luo, Yunan, et al.. (2024). Leveraging conformal prediction to annotate enzyme function space with limited false positives. PLoS Computational Biology. 20(5). e1012135–e1012135. 2 indexed citations
8.
9.
Li, Mufei, Zhongyuan Zhao, J. Duarte, et al.. (2024). Opportunities and challenges of graph neural networks in electrical engineering. 1(8). 529–546. 5 indexed citations
10.
Qian, Wesley Wei, Jennifer N. Wei, Benjamín Sánchez-Lengeling, et al.. (2023). Metabolic activity organizes olfactory representations. eLife. 12. 11 indexed citations
11.
Wang, Sheng, et al.. (2023). Supervised biological network alignment with graph neural networks. Bioinformatics. 39(Supplement_1). i465–i474. 2 indexed citations
12.
Luo, Yunan, Yang Liu, & Jian Peng. (2023). Calibrated geometric deep learning improves kinase–drug binding predictions. Nature Machine Intelligence. 5(12). 1390–1401. 25 indexed citations
13.
Yu, Tianhao, Haiyang Cui, Jianan Canal Li, et al.. (2023). Enzyme function prediction using contrastive learning. Science. 379(6639). 1358–1363. 236 indexed citations breakdown →
14.
Luo, Yunan. (2023). Sensing the shape of functional proteins with topology. Nature Computational Science. 3(2). 124–125. 3 indexed citations
15.
Zhang, Ruiyi, Yunan Luo, Jianzhu Ma, Ming Zhang, & Sheng Wang. (2022). scPretrain: multi-task self-supervised learning for cell-type classification. Bioinformatics. 38(6). 1607–1614. 10 indexed citations
16.
Liu, Xianggen, Yunan Luo, Pengyong Li, Sen Song, & Jian Peng. (2021). Deep geometric representations for modeling effects of mutations on protein-protein binding affinity. PLoS Computational Biology. 17(8). e1009284–e1009284. 81 indexed citations
17.
Luo, Yunan, Guangde Jiang, Tianhao Yu, et al.. (2021). ECNet is an evolutionary context-integrated deep learning framework for protein engineering. Nature Communications. 12(1). 5743–5743. 112 indexed citations
18.
Su, Yufeng, et al.. (2019). Integrating thermodynamic and sequence contexts improves protein-RNA binding prediction. PLoS Computational Biology. 15(9). e1007283–e1007283. 21 indexed citations
19.
Luo, Yunan, Yun William Yu, Jianyang Zeng, Bonnie Berger, & Jian Peng. (2018). Metagenomic binning through low-density hashing. Bioinformatics. 35(2). 219–226. 18 indexed citations
20.
Luo, Yunan, Xinbin Zhao, Jingtian Zhou, et al.. (2017). A Network Integration Approach for Drug-Target Interaction Prediction and Computational Drug Repositioning from Heterogeneous Information.. RePEc: Research Papers in Economics. 383–384. 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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