Weizhong Zhao

2.0k total citations
64 papers, 829 citations indexed

About

Weizhong Zhao is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Weizhong Zhao has authored 64 papers receiving a total of 829 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Molecular Biology, 32 papers in Artificial Intelligence and 12 papers in Computational Theory and Mathematics. Recurrent topics in Weizhong Zhao's work include Topic Modeling (19 papers), Biomedical Text Mining and Ontologies (19 papers) and Computational Drug Discovery Methods (12 papers). Weizhong Zhao is often cited by papers focused on Topic Modeling (19 papers), Biomedical Text Mining and Ontologies (19 papers) and Computational Drug Discovery Methods (12 papers). Weizhong Zhao collaborates with scholars based in China, United States and Taiwan. Weizhong Zhao's co-authors include Wen Zou, James J. Chen, Roger Perkins, Yijun Ding, Zhichao Liu, Weigong Ge, Zhixin Li, Jinxiang Xi, Xingpeng Jiang and Huifang Ma and has published in prestigious journals such as Bioinformatics, PLoS ONE and Expert Systems with Applications.

In The Last Decade

Weizhong Zhao

54 papers receiving 807 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Weizhong Zhao China	16	312	130	109	96	80	64	829
Zhichao Liu China	15	360 1.2×	163 1.3×	51 0.5×	117 1.2×	58 0.7×	42	897
Matthew Hutson Canada	15	393 1.3×	99 0.8×	78 0.7×	44 0.5×	12 0.1×	77	1.1k
Brian Mac Namee Ireland	19	691 2.2×	54 0.4×	171 1.6×	166 1.7×	23 0.3×	118	1.6k
Shoko Wakamiya Japan	15	213 0.7×	74 0.6×	81 0.7×	55 0.6×	10 0.1×	83	761
Zhi-Hua Zhou China	8	483 1.5×	88 0.7×	235 2.2×	201 2.1×	32 0.4×	8	1.1k
James R. Foulds United States	16	591 1.9×	174 1.3×	194 1.8×	207 2.2×	33 0.4×	41	1.1k
Lu An China	13	271 0.9×	47 0.4×	119 1.1×	170 1.8×	7 0.1×	76	679
Kiran Khatter India	8	445 1.4×	41 0.3×	140 1.3×	66 0.7×	12 0.1×	23	923
Gisele L. Pappa Brazil	22	700 2.2×	129 1.0×	232 2.1×	78 0.8×	20 0.3×	132	1.5k

Countries citing papers authored by Weizhong Zhao

Since Specialization

Citations

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

Fields of papers citing papers by Weizhong Zhao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Weizhong Zhao

This figure shows the co-authorship network connecting the top 25 collaborators of Weizhong Zhao. A scholar is included among the top collaborators of Weizhong 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 Weizhong Zhao. Weizhong Zhao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Xiong, Yan, et al.. (2025). AFR-Rank: An effective and highly efficient LLM-based listwise reranking framework via filtering noise documents. Information Processing & Management. 62(6). 104232–104232.

Yao, Wenjie, et al.. (2024). An Improved Framework for Drug-Side Effect Associations Prediction via Counterfactual Inference-Based Data Augmentation. IEEE Transactions on NanoBioscience. 23(4). 540–547.

Zhao, Weizhong, et al.. (2024). A Novel Framework for Predicting Phage-Host Interactions via Host Specificity-Aware Graph Autoencoder. IEEE Journal of Biomedical and Health Informatics. 29(4). 3069–3078. 1 indexed citations

Zhao, Weizhong, et al.. (2024). A novel framework for phage-host prediction via logical probability theory and network sparsification. Briefings in Bioinformatics. 26(1).

Fu, Lingling, et al.. (2024). Predicting phage–host interactions via feature augmentation and regional graph convolution. Briefings in Bioinformatics. 26(1). 1 indexed citations

Jiang, Xingpeng, et al.. (2024). AMPpred-DLFF:prediction of AMPs based on deep learning and multi-view features fusion. 891–896. 1 indexed citations

Zhao, Weizhong, Xianjun Shen, Xingpeng Jiang, et al.. (2023). Improving drug–drug interactions prediction with interpretability via meta-path-based information fusion. Briefings in Bioinformatics. 24(2). 10 indexed citations

Zhao, Weizhong, et al.. (2023). An effective multi-task learning framework for drug repurposing based on graph representation learning. Methods. 218. 48–56. 2 indexed citations

Li, Dandan, et al.. (2023). Prediction of Drug–Disease Associations Based on Multi-Kernel Deep Learning Method in Heterogeneous Graph Embedding. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 21(1). 120–128. 4 indexed citations

10.

Zhao, Weizhong, et al.. (2023). Effective Drug Repositioning with a Novel Negative Sample Selection Algorithm. 739–744.

11.

Li, Dandan, et al.. (2023). An Effective Model for Predicting Phage-Host Interactions Via Graph Embedding Representation Learning With Multi-Head Attention Mechanism. IEEE Journal of Biomedical and Health Informatics. 27(6). 3061–3071. 6 indexed citations

12.

Zhao, Weizhong, et al.. (2021). Efficient multiple biomedical events extraction via reinforcement learning. Bioinformatics. 37(13). 1891–1899. 9 indexed citations

13.

Xi, Jinxiang & Weizhong Zhao. (2019). Correlating exhaled aerosol images to small airway obstructive diseases: A study with dynamic mode decomposition and machine learning. PLoS ONE. 14(1). e0211413–e0211413. 28 indexed citations

14.

Zou, Wen, Weizhong Zhao, James J. Chen, & Roger Perkins. (2017). Best Setting of Model Parameters in Applying Topic Modeling on Textual Documents.. 588–588.

15.

Xi, Jinxiang, et al.. (2017). Multi-resolution classification of exhaled aerosol images to detect obstructive lung diseases in small airways. Computers in Biology and Medicine. 87. 57–69. 11 indexed citations

16.

Zhao, Weizhong, James J. Chen, Roger Perkins, et al.. (2016). A novel procedure on next generation sequencing data analysis using text mining algorithm. BMC Bioinformatics. 17(1). 213–213. 15 indexed citations

17.

Wang, Shi‐Heng, Yijun Ding, Weizhong Zhao, et al.. (2016). Text mining for identifying topics in the literatures about adolescent substance use and depression. BMC Public Health. 16(1). 279–279. 75 indexed citations

18.

Zhao, Weizhong, James J. Chen, Roger Perkins, et al.. (2015). A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics. 16(S13). S8–S8. 254 indexed citations

19.

Zou, Wen, Hailin Tang, Weizhong Zhao, et al.. (2013). Data mining tools for Salmonella characterization: application to gel-based fingerprinting analysis. BMC Bioinformatics. 14(S14). S15–S15. 18 indexed citations

20.

Zhang, Ai‐bing, Robert Ward, Ping Wan, et al.. (2012). A New Method for Species Identification via Protein-Coding and Non-Coding DNA Barcodes by Combining Machine Learning with Bioinformatic Methods. PLoS ONE. 7(2). e30986–e30986. 29 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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