Bo-Wei Zhao

2.5k total citations
91 papers, 1.7k citations indexed

About

Bo-Wei Zhao is a scholar working on Molecular Biology, Condensed Matter Physics and Electronic, Optical and Magnetic Materials. According to data from OpenAlex, Bo-Wei Zhao has authored 91 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Molecular Biology, 38 papers in Condensed Matter Physics and 20 papers in Electronic, Optical and Magnetic Materials. Recurrent topics in Bo-Wei Zhao's work include Physics of Superconductivity and Magnetism (34 papers), Bioinformatics and Genomic Networks (19 papers) and Magnetic and transport properties of perovskites and related materials (18 papers). Bo-Wei Zhao is often cited by papers focused on Physics of Superconductivity and Magnetism (34 papers), Bioinformatics and Genomic Networks (19 papers) and Magnetic and transport properties of perovskites and related materials (18 papers). Bo-Wei Zhao collaborates with scholars based in China, Belgium and Hong Kong. Bo-Wei Zhao's co-authors include Xiaorui Su, Lun Hu, Zhu‐Hong You, Pengwei Hu, Lei Wang, Leon Wong, Yue Yang, Guodong Li, Bei Zhu and Lei Wang and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Physical review. B, Condensed matter.

In The Last Decade

Bo-Wei Zhao

89 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bo-Wei Zhao China 23 900 522 288 257 228 91 1.7k
John A. Board United States 14 534 0.6× 135 0.3× 82 0.3× 348 1.4× 16 0.1× 36 1.8k
Evangelos A. Coutsias United States 21 836 0.9× 277 0.5× 35 0.1× 410 1.6× 5 0.0× 66 1.6k
Seung‐Yeon Kim South Korea 20 483 0.5× 84 0.2× 502 1.7× 332 1.3× 9 0.0× 107 1.2k
Michael Harder Canada 23 225 0.3× 48 0.1× 248 0.9× 217 0.8× 4 0.0× 46 2.3k
L. Guidoni France 19 148 0.2× 20 0.0× 126 0.4× 184 0.7× 35 0.2× 91 1.8k
Alexander L. Gaunt United Kingdom 15 81 0.1× 88 0.2× 238 0.8× 221 0.9× 8 0.0× 26 1.7k
Gary Huber United States 20 867 1.0× 86 0.2× 47 0.2× 204 0.8× 14 0.1× 65 1.5k
Tetsuji Tokihiro Japan 23 98 0.1× 244 0.5× 274 1.0× 470 1.8× 2 0.0× 95 1.9k
László Tóth Hungary 17 85 0.1× 77 0.1× 77 0.3× 94 0.4× 11 0.0× 103 1.4k
Kelin Xia Singapore 26 1.0k 1.1× 1.1k 2.2× 13 0.0× 329 1.3× 11 0.0× 92 2.0k

Countries citing papers authored by Bo-Wei Zhao

Since Specialization
Citations

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

Fields of papers citing papers by Bo-Wei Zhao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bo-Wei Zhao. 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 Bo-Wei Zhao. The network helps show where Bo-Wei Zhao may publish in the future.

Co-authorship network of co-authors of Bo-Wei Zhao

This figure shows the co-authorship network connecting the top 25 collaborators of Bo-Wei Zhao. A scholar is included among the top collaborators of Bo-Wei Zhao 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 Bo-Wei Zhao. Bo-Wei Zhao 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.
Li, Guodong, Bo-Wei Zhao, Xiaorui Su, et al.. (2025). Capturing short-range and long-range dependencies of nucleotides for identifying RNA N6-methyladenosine modification sites. Computers in Biology and Medicine. 186. 109625–109625.
2.
Wei, Mengmeng, et al.. (2025). Integrating Transformer and Graph Attention Network for circRNA-miRNA Interaction Prediction. IEEE Journal of Biomedical and Health Informatics. 29(8). 6105–6113. 5 indexed citations
3.
Li, Dongxu, et al.. (2025). DeepHIV: A Sequence-Based Deep Learning Model for Predicting HIV-1 Protease Cleavage Sites. PubMed. 22(6). 3557–3563. 2 indexed citations
4.
Zhao, Bo-Wei, Xiaorui Su, Dongxu Li, et al.. (2024). Regulation-aware graph learning for drug repositioning over heterogeneous biological network. Information Sciences. 686. 121360–121360. 46 indexed citations
5.
Gao, Yue, et al.. (2024). RESEARCH ON CONSTRUCTION OF NATURAL RESOURCES THREE-DIMENSIONAL SPATIO-TEMPORAL DATABASE SYSTEM. SHILAP Revista de lepidopterología. XLVIII-4/W9-2024. 175–182. 1 indexed citations
6.
Wang, K., et al.. (2024). UBE2T is a diagnostic and prognostic biomarker for endometrial cancer. Clinical & Translational Oncology. 27(5). 2067–2083. 1 indexed citations
7.
Wang, Lei, et al.. (2024). A PiRNA-disease association model incorporating sequence multi-source information with graph convolutional networks. Applied Soft Computing. 157. 111523–111523. 10 indexed citations
8.
Wang, Lei, Zhu‐Hong You, Chang-Qing Yu, et al.. (2024). Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA–miRNA associations. Briefings in Bioinformatics. 25(2). 20 indexed citations
9.
Zhao, Bo-Wei, Xiaorui Su, Yue Yang, et al.. (2024). Motif-Aware miRNA-Disease Association Prediction via Hierarchical Attention Network. IEEE Journal of Biomedical and Health Informatics. 28(7). 4281–4294. 22 indexed citations
10.
Li, Guodong, Bo-Wei Zhao, Xiaorui Su, et al.. (2024). Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering. IEEE Journal of Biomedical and Health Informatics. 28(4). 2362–2372. 39 indexed citations
11.
Zhao, Bo-Wei, Xiaorui Su, Yue Yang, et al.. (2024). A heterogeneous information network learning model with neighborhood-level structural representation for predicting lncRNA-miRNA interactions. Computational and Structural Biotechnology Journal. 23. 2924–2933. 28 indexed citations
12.
Li, Guodong, Bo-Wei Zhao, Xiaorui Su, et al.. (2024). Learning Sequential and Structural Dependencies Between Nucleotides for RNA N6-Methyladenosine Site Identification. IEEE/CAA Journal of Automatica Sinica. 11(10). 2123–2134. 1 indexed citations
13.
Wang, Sile, Xiaorui Su, Bo-Wei Zhao, et al.. (2023). An Improved Graph Isomorphism Network for Accurate Prediction of Drug–Drug Interactions. Mathematics. 11(18). 3990–3990. 6 indexed citations
14.
Li, Dongxu, Peng Zhou, Bo-Wei Zhao, et al.. (2023). Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks. BMC Bioinformatics. 24(1). 451–451. 1 indexed citations
15.
Chen, Zhan‐Heng, et al.. (2023). GraphCPIs: A novel graph-based computational model for potential compound-protein interactions. Molecular Therapy — Nucleic Acids. 32. 721–728. 9 indexed citations
16.
Yang, Yue, Xiaorui Su, Bo-Wei Zhao, et al.. (2023). Fuzzy-Based Deep Attributed Graph Clustering. IEEE Transactions on Fuzzy Systems. 32(4). 1951–1964. 63 indexed citations
17.
Zhao, Bo-Wei, Lei Wang, Pengwei Hu, et al.. (2023). Fusing Higher and Lower-Order Biological Information for Drug Repositioning via Graph Representation Learning. IEEE Transactions on Emerging Topics in Computing. 12(1). 163–176. 70 indexed citations
18.
Zhao, Bo-Wei, et al.. (2022). A geometric deep learning framework for drug repositioning over heterogeneous information networks. Briefings in Bioinformatics. 23(6). 82 indexed citations
19.
You, Zhu‐Hong, Lei Wang, Chang-Qing Yu, et al.. (2022). A novel circRNA-miRNA association prediction model based on structural deep neural network embedding. Briefings in Bioinformatics. 23(5). 41 indexed citations
20.
Su, Xiaorui, Zhu‐Hong You, Lei Wang, et al.. (2021). SANE: A sequence combined attentive network embedding model for COVID-19 drug repositioning. Applied Soft Computing. 111. 107831–107831. 23 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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