Beilun Wang

635 total citations · 1 hit paper
27 papers, 265 citations indexed

About

Beilun Wang is a scholar working on Artificial Intelligence, Molecular Biology and Statistics and Probability. According to data from OpenAlex, Beilun Wang has authored 27 papers receiving a total of 265 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 10 papers in Molecular Biology and 3 papers in Statistics and Probability. Recurrent topics in Beilun Wang's work include Gene expression and cancer classification (6 papers), Adversarial Robustness in Machine Learning (5 papers) and Metabolomics and Mass Spectrometry Studies (3 papers). Beilun Wang is often cited by papers focused on Gene expression and cancer classification (6 papers), Adversarial Robustness in Machine Learning (5 papers) and Metabolomics and Mass Spectrometry Studies (3 papers). Beilun Wang collaborates with scholars based in China, United States and Australia. Beilun Wang's co-authors include Yanjun Qi, Ritambhara Singh, Jack Lanchantin, Ji Gao, Chunshu Li, Meng Wang, Xinyu Ma, Shaowen Zhu, Jingyu Kuang and Xing Liu and has published in prestigious journals such as IEEE Access, Sensors and Neurocomputing.

In The Last Decade

Beilun Wang

22 papers receiving 255 citations

Hit Papers

align trajectories

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.

Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens

2025 24 citations Beilun Wang, Yi Zhong et al. Nature Microbiology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Beilun Wang China	9	101	95	33	32	19	27	265
Mudassir Shabbir Pakistan	10	33 0.3×	94 1.0×	19 0.6×	26 0.8×	72 3.8×	41	225
Inge Li Gørtz Denmark	7	47 0.5×	120 1.3×	15 0.5×	14 0.4×	16 0.8×	42	158
Xiaodong Duan China	10	109 1.1×	43 0.5×	7 0.2×	23 0.7×	17 0.9×	33	279
Benjamin Sach United Kingdom	7	37 0.4×	89 0.9×	14 0.4×	26 0.8×	34 1.8×	13	168
Jiawei Han China	5	48 0.5×	105 1.1×	41 1.2×	57 1.8×	15 0.8×	9	168
Zheng Gao United States	8	45 0.4×	96 1.0×	68 2.1×	27 0.8×	35 1.8×	36	220
Murillo G. Carneiro Brazil	10	50 0.5×	95 1.0×	15 0.5×	34 1.1×	26 1.4×	50	245
Andrej Dobnikar Slovenia	9	96 1.0×	87 0.9×	13 0.4×	36 1.1×	18 0.9×	29	267
Esraa Alhenawi Jordan	7	69 0.7×	122 1.3×	37 1.1×	31 1.0×	71 3.7×	22	234

Countries citing papers authored by Beilun Wang

Since Specialization

Citations

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

Fields of papers citing papers by Beilun Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Beilun Wang

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

Yang, Bin, Dian Shen, Junxue Zhang, et al.. (2025). eNetSTL: Towards an In-kernel Library for High-Performance eBPF-based Network Functions. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 42–58. 3 indexed citations

Wang, Beilun, et al.. (2025). Structured reflective reasoning for precise medical knowledge graph retrieval augmented generation. Health Information Science and Systems. 13(1). 76–76.

Wang, Beilun, Chang Wang, Jingyu Kuang, et al.. (2025). A two-stage metabolome refining pipeline for natural products discovery. Synthetic and Systems Biotechnology. 10(2). 600–609.

Wang, Beilun, Yi Zhong, Xiao Tan, et al.. (2025). Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens. Nature Microbiology. 10(2). 332–347. 24 indexed citations breakdown →

Wang, Shidong, et al.. (2024). Exploiting biochemical data to improve osteosarcoma diagnosis with deep learning. Health Information Science and Systems. 12(1). 31–31. 2 indexed citations

Zhang, Jinghui, Shikun Feng, Yu Sun, et al.. (2023). Label Information Enhanced Fraud Detection against Low Homophily in Graphs. arXiv (Cornell University). 406–416. 25 indexed citations

Zhang, Yan, Cheng Chen, Dian Shen, Meng Wang, & Beilun Wang. (2023). Take CARE: Improving Inherent Robustness of Spiking Neural Networks with Channel-wise Activation Recalibration Module. 828–837.

Wang, Beilun, et al.. (2022). Fast and scalable learning of sparse changes in high-dimensional graphical model structure. Neurocomputing. 514. 39–57. 1 indexed citations

Wang, Beilun, et al.. (2022). An End-to-End Mutually Interactive Emotion–Cause Pair Extractor via Soft Sharing. Applied Sciences. 12(18). 8998–8998. 1 indexed citations

10.

Liu, Xing, et al.. (2021). Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study. JMIR Medical Informatics. 9(6). e28277–e28277. 14 indexed citations

11.

Wang, Beilun, et al.. (2021). Scalable Estimator for Multi-task Gaussian Graphical Models Based in an IoT Network. ACM Transactions on Sensor Networks. 17(3). 1–33. 2 indexed citations

12.

Wang, Beilun, et al.. (2020). How Decisions Are Made in Brains: Unpack “Black Box” of CNN With Ms. Pac-Man Video Game. IEEE Access. 8. 142446–142458. 12 indexed citations

13.

Wang, Beilun, et al.. (2018). A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models. International Conference on Machine Learning. 5148–5157. 1 indexed citations

14.

Wang, Beilun, Ji Gao, & Yanjun Qi. (2017). A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples. International Conference on Learning Representations. 8 indexed citations

15.

Gao, Ji, Beilun Wang, Zeming Lin, Weilin Xu, & Yanjun Qi. (2017). DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples. International Conference on Learning Representations. 4 indexed citations

16.

Wang, Beilun, et al.. (2017). Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure. International Conference on Artificial Intelligence and Statistics. 1691–1700. 1 indexed citations

17.

Wang, Beilun, Ji Gao, & Yanjun Qi. (2017). A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models. International Conference on Artificial Intelligence and Statistics. 1168–1177. 2 indexed citations

18.

Gao, Ji, Beilun Wang, & Yanjun Qi. (2017). DeepMask: Masking DNN Models for robustness against adversarial samples.. arXiv (Cornell University). 6 indexed citations

19.

Wang, Beilun, Ritambhara Singh, & Yanjun Qi. (2017). A constrained $$\ell $$1 minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models. Machine Learning. 106(9-10). 1381–1417. 6 indexed citations

20.

Wang, Beilun, Ji Gao, & Yanjun Qi. (2016). A Theoretical Framework for Robustness of (Deep) Classifiers Under Adversarial Noise.. arXiv (Cornell University). 7 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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