Shanfeng Zhu

5.8k total citations
94 papers, 2.7k citations indexed

About

Shanfeng Zhu is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Shanfeng Zhu has authored 94 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 67 papers in Molecular Biology, 26 papers in Artificial Intelligence and 11 papers in Computational Theory and Mathematics. Recurrent topics in Shanfeng Zhu's work include Biomedical Text Mining and Ontologies (28 papers), Machine Learning in Bioinformatics (22 papers) and Bioinformatics and Genomic Networks (22 papers). Shanfeng Zhu is often cited by papers focused on Biomedical Text Mining and Ontologies (28 papers), Machine Learning in Bioinformatics (22 papers) and Bioinformatics and Genomic Networks (22 papers). Shanfeng Zhu collaborates with scholars based in China, Japan and Australia. Shanfeng Zhu's co-authors include Hiroshi Mamitsuka, Ronghui You, Hao Ding, Xiaodi Huang, Ichigaku Takigawa, Xiaodong Zheng, Fengzhu Sun, Yi Xiong, Shuwei Yao and Jia Zeng and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.

In The Last Decade

Shanfeng Zhu

90 papers receiving 2.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shanfeng Zhu China 27 1.9k 839 639 216 163 94 2.7k
Shaoliang Peng China 28 1.5k 0.8× 374 0.4× 441 0.7× 70 0.3× 122 0.7× 190 3.1k
Hiroshi Mamitsuka Japan 35 2.7k 1.4× 1.0k 1.2× 1.1k 1.8× 365 1.7× 210 1.3× 180 4.3k
Jianjun Qi China 29 1.4k 0.7× 1.4k 1.6× 741 1.2× 120 0.6× 135 0.8× 124 3.5k
Ali Masoudi‐Nejad Iran 34 2.4k 1.2× 961 1.1× 330 0.5× 89 0.4× 222 1.4× 165 3.7k
Ola Spjuth Sweden 31 1.5k 0.8× 1.2k 1.4× 300 0.5× 74 0.3× 332 2.0× 125 2.9k
Jason D. Hughes United States 14 4.3k 2.2× 489 0.6× 364 0.6× 290 1.3× 137 0.8× 33 5.5k
Bin Liu China 50 7.7k 4.0× 807 1.0× 428 0.7× 177 0.8× 149 0.9× 165 8.9k
Parantu K. Shah United States 19 1.8k 0.9× 851 1.0× 391 0.6× 219 1.0× 424 2.6× 42 3.1k
Jianzhu Ma United States 27 2.2k 1.2× 489 0.6× 309 0.5× 139 0.6× 337 2.1× 69 3.1k
Yaohang Li United States 34 2.4k 1.2× 1.5k 1.8× 534 0.8× 48 0.2× 394 2.4× 144 3.8k

Countries citing papers authored by Shanfeng Zhu

Since Specialization
Citations

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

Fields of papers citing papers by Shanfeng Zhu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shanfeng Zhu

This figure shows the co-authorship network connecting the top 25 collaborators of Shanfeng Zhu. A scholar is included among the top collaborators of Shanfeng Zhu 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 Shanfeng Zhu. Shanfeng Zhu 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.
Wang, Ziye, et al.. (2025). Benchmarking metagenomic binning tools on real datasets across sequencing platforms and binning modes. Nature Communications. 16(1). 2865–2865. 3 indexed citations
2.
Zhu, Shanfeng, Heng Wang, Chun Sun, et al.. (2025). Fiber-Optic Photoacoustic Gas Sensor Based on a Miniaturized Multi-pass Cell with an Elliptical Cross-section. Journal of Analysis and Testing. 10(1). 273–283. 3 indexed citations
3.
Zhu, Shanfeng, Wen Tang, Yuting Xie, & Lexing Xie. (2025). A comprehensive review of federated learning for multi-center medical data. 4(2). 9–9. 1 indexed citations
4.
Wang, Ziye, et al.. (2024). Effective binning of metagenomic contigs using contrastive multi-view representation learning. Nature Communications. 15(1). 585–585. 26 indexed citations
5.
Bian, J. M., et al.. (2024). One-shot Biomedical Named Entity Recognition via Knowledge-Inspired Large Language Model. 1–10. 3 indexed citations
6.
Huang, Xiaodi, et al.. (2023). Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities. Briefings in Bioinformatics. 24(4). 7 indexed citations
7.
Qu, Wei, Ronghui You, Hiroshi Mamitsuka, & Shanfeng Zhu. (2023). DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction. Bioinformatics. 39(9). 5 indexed citations
8.
Li, Huang, et al.. (2021). GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction. 2674–2685. 1 indexed citations
9.
Huang, Xiaodi, et al.. (2020). HPOLabeler: improving prediction of human protein–phenotype associations by learning to rank. Bioinformatics. 36(14). 4180–4188. 20 indexed citations
10.
You, Ronghui, Yuxuan Liu, Hiroshi Mamitsuka, & Shanfeng Zhu. (2020). BERTMeSH: deep contextual representation learning for large-scale high-performance MeSH indexing with full text. Bioinformatics. 37(5). 684–692. 24 indexed citations
11.
You, Ronghui, Shuwei Yao, Yi Xiong, et al.. (2019). NetGO: improving large-scale protein function prediction with massive network information. Nucleic Acids Research. 47(W1). W379–W387. 90 indexed citations
12.
You, Ronghui, Zihan Zhang, Yi Xiong, et al.. (2018). GOLabeler: improving sequence-based large-scale protein function prediction by learning to rank. Bioinformatics. 34(14). 2465–2473. 123 indexed citations
13.
Bai, Xin, Meng Fang, Shipeng Chen, et al.. (2018). Deep sequencing of HBV pre-S region reveals high heterogeneity of HBV genotypes and associations of word pattern frequencies with HCC. PLoS Genetics. 14(2). e1007206–e1007206. 11 indexed citations
14.
You, Ronghui, Suyang Dai, Zihan Zhang, Hiroshi Mamitsuka, & Shanfeng Zhu. (2018). AttentionXML: Extreme Multi-Label Text Classification with Multi-Label Attention Based Recurrent Neural Networks.. arXiv (Cornell University). 19 indexed citations
15.
Mamitsuka, Hiroshi, et al.. (2018). MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing. Methods in molecular biology. 1807. 203–209. 2 indexed citations
16.
Yamada, Makoto, et al.. (2016). A robust convex formulation for ensemble clustering. Aaltodoc (Aalto University). 1476–1482. 3 indexed citations
17.
Yamada, Makoto, et al.. (2016). Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16). International Joint Conference on Artificial Intelligence. 38 indexed citations
18.
You, Ronghui, et al.. (2015). FDUMedSearch at TREC 2015 Clinical Decision Support Track.. Text REtrieval Conference. 3 indexed citations
19.
Zheng, Xiaodong, et al.. (2015). Instance-wise weighted nonnegative matrix factorization for aggregating partitions with locally reliable clusters. Kyoto University Research Information Repository (Kyoto University). 4091–4097. 11 indexed citations
20.
Zhang, Yanchun, et al.. (2015). The Fudan Participation in the 2015 BioASQ Challenge: Large-scale Biomedical Semantic Indexing and Question Answering. Victoria University Research Repository (Victoria University). 18 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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