Juan Liu
- Molecular Biology top 10%
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Epidemiology
- Topics
- Bioinformatics and Genomic Networks (40 papers)Computational Drug Discovery Methods (28 papers)Gene expression and cancer classification (27 papers)
- Journals
- Journal of the American Chemical SocietyGenes & DevelopmentEnvironmental Science & Technology
- Partner nations
- ChinaUnited StatesJapan
In The Last Decade
Juan Liu
162 papers receiving 2.1k citations
Peers
Comparison fields: 5 of 168
- Molecular Biology 1.2k
- Artificial Intelligence 433
- Computational Theory and Mathematics 305
- Radiology, Nuclear Medicine and Imaging 255
- Epidemiology 187
Countries citing papers authored by Juan Liu
This map shows the geographic impact of Juan 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 Juan Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Juan Liu more than expected).
Fields of papers citing papers by Juan Liu
This network shows the impact of papers produced by Juan 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 Juan Liu. The network helps show where Juan Liu may publish in the future.
Co-authorship network of co-authors of Juan Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Juan Liu. A scholar is included among the top collaborators of Juan 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 Juan Liu. Juan 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 | 1 | |
| 4 | 4 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 5 | |
| 8 | 0 | |
| 9 | 1 | |
| 10 | 12 | |
| 11 | 34 | |
| 12 | 2 | |
| 13 | 3 | |
| 14 | 3 | |
| 15 | 25 | |
| 16 | 1 | |
| 17 | Mutation of NPHS1 gene in a Chinese child with congenital nephrotic syndrome | 1 |
| 18 | Designing an intelligent recommender system using partial credit model and bayesian rough set. | 7 |
| 19 | Multi-Class Protein Fold Recognition Using Relevance Vector Machine | 1 |
| 20 | 1 |
About Juan Liu
Juan Liu is a scholar working on Computational Theory and Mathematics, Molecular Biology and Cancer Research, having authored 168 papers that have together received 2.2k indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (40 papers), Computational Drug Discovery Methods (28 papers) and Gene expression and cancer classification (27 papers). The work is most often cited by research in Health Informatics (31 citations), Computational Theory and Mathematics (305 citations) and Molecular Biology (1.2k citations). Juan Liu has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Yi Xiong, Wen Zhang, Tao Zeng, Jing Feng, Luonan Chen, Wenwen Min, Hua Chen, Shihua Zhang, Guangsheng Wu and Meng Zhao. Their work appears in journals such as Journal of the American Chemical Society, Genes & Development and Environmental Science & Technology.
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.