Zongying Liu
- Artificial Intelligence top 10%
- Molecular Biology
- Epidemiology
- Public Health, Environmental and Occupational Health
- Electrical and Electronic Engineering
- Co-authors
- Chu Kiong LooKitsuchart PasupaManjeevan SeeraMingyang PanNaoki MasuyamaWangbing ShenAbdelbasset A. FarahatDavid W. Boykin
- Topics
- Machine Learning and ELM (12 papers)Neural Networks and Applications (9 papers)Advanced Memory and Neural Computing (7 papers)
- Journals
- PLoS ONEIEEE AccessNeurocomputing
In The Last Decade
Zongying Liu
33 papers receiving 321 citations
Peers
Comparison fields: 5 of 110
- Artificial Intelligence 92
- Molecular Biology 62
- Epidemiology 56
- Public Health, Environmental and Occupational Health 48
- Electrical and Electronic Engineering 39
Countries citing papers authored by Zongying Liu
This map shows the geographic impact of Zongying Liu'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 Zongying Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zongying Liu more than expected).
Fields of papers citing papers by Zongying Liu
This network shows the impact of papers produced by Zongying Liu. 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 Zongying Liu. The network helps show where Zongying Liu may publish in the future.
Co-authorship network of co-authors of Zongying Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Zongying Liu. A scholar is included among the top collaborators of Zongying Liu 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 Zongying Liu. Zongying Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 5 | |
| 4 | 1 | |
| 5 | 5 | |
| 6 | 2 | |
| 7 | 4 | |
| 8 | 2 | |
| 9 | 12 | |
| 10 | 6 | |
| 11 | 18 | |
| 12 | 25 | |
| 13 | 1 | |
| 14 | Blood DNA methylation markers in potentially identified Chinese patients with hepatocellular carcinoma. | 5 |
| 15 | 21 | |
| 16 | 37 | |
| 17 | 9 | |
| 18 | 5 | |
| 19 | 31 | |
| 20 | Screening and synthesis of influenza neuraminidase inhibitors | 0 |
About Zongying Liu
Zongying Liu is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology and Health Information Management, having authored 39 papers that have together received 326 indexed citations. Recurring topics across this work include Machine Learning and ELM (12 papers), Neural Networks and Applications (9 papers) and Advanced Memory and Neural Computing (7 papers). The work is most often cited by research in Artificial Intelligence (92 citations), Dermatology (16 citations) and Public Health, Environmental and Occupational Health (48 citations). Zongying Liu has collaborated with scholars based in China, Malaysia and Thailand. Frequent co-authors include Chu Kiong Loo, Kitsuchart Pasupa, Manjeevan Seera, Mingyang Pan, Naoki Masuyama, Wangbing Shen, Abdelbasset A. Farahat, David W. Boykin, Arvind Kumar and Yan Ding. Their work appears in journals such as PLoS ONE, IEEE Access and Neurocomputing.
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.