Ying‐Ju Chen
- Health Informatics top 2%
- Food Science top 5%
- Essential Oils and Antimicrobial Activity 8
- Sensory Systems top 5%
- Insect Science top 5%
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- Viral Infectious Diseases and Gene Expression in Insects 8
- Insect Resistance and Genetics 6
- Natural product bioactivities and synthesis 4
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- Statistical Distribution Estimation and Applications 6
- Statistical Methods and Bayesian Inference 5
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- Phytochemistry and Biological Activities 5
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- Advanced X-ray Imaging Techniques 4
- Co-authors
- Shang‐Tzen ChangFadel M. MegahedSen‐Sung ChengChin‐Gi HuangWei‐June ChenTzong‐Yuan WuChia-Wei HuangWei‐Hsin Sun
- Partner nations
- TaiwanUnited StatesChina
In The Last Decade
Ying‐Ju Chen
118 papers receiving 2.4k citations
Hit Papers
Peers
Comparison fields: 5 of 195
- Health Informatics 60
- Business and International Management 32
- Food Science 251
- Sensory Systems 63
- Insect Science 147
Countries citing papers authored by Ying‐Ju Chen
This map shows the geographic impact of Ying‐Ju Chen'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 Ying‐Ju Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ying‐Ju Chen more than expected).
Fields of papers citing papers by Ying‐Ju Chen
This network shows the impact of papers produced by Ying‐Ju Chen. 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 Ying‐Ju Chen. The network helps show where Ying‐Ju Chen may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ying‐Ju Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 9 | |
| 6 | 2024 | 12 | |
| 7 | 2024 | 0 | |
| 8 | 2024 | 2 | |
| 9 | 2024 | 1 | |
| 10 | How generative AI models such as ChatGPT can be (mis)used in SPC practice, education, and research? An exploratory studybreakdown → | 2023 | 104 |
| 11 | 2023 | 4 | |
| 12 | 2023 | 17 | |
| 13 | 2021 | 5 | |
| 14 | 2020 | 5 | |
| 15 | 2016 | 8 | |
| 16 | An evaluation of fatty acid-CoA ligase 4 in breast cancer. | 2014 | 2 |
| 17 | Stipules and Colleters of the Mangrove Rhizophoraceae: Morphology, Structure and Comparative Significance | 2012 | 13 |
| 18 | 2009 | 18 | |
| 19 | 2008 | 7 | |
| 20 | 2005 | 40 |
About Ying‐Ju Chen
Ying‐Ju Chen is a scholar working on Statistics and Probability, Health Informatics and Rehabilitation, having authored 122 papers that have together received 2.5k indexed citations. Recurring topics across this work include Essential Oils and Antimicrobial Activity (8 papers), Viral Infectious Diseases and Gene Expression in Insects (8 papers), Statistical Distribution Estimation and Applications (6 papers), Insect Resistance and Genetics (6 papers), Statistical Methods and Bayesian Inference (5 papers), Phytochemistry and Biological Activities (5 papers), Natural product bioactivities and synthesis (4 papers) and Advanced X-ray Imaging Techniques (4 papers). The work is most often cited by research in Health Informatics (60 citations), Business and International Management (32 citations) and Food Science (251 citations). Ying‐Ju Chen has collaborated with scholars based in Taiwan, United States and China. Frequent co-authors include Shang‐Tzen Chang, Fadel M. Megahed, Sen‐Sung Cheng, Chin‐Gi Huang, Wei‐June Chen, Tzong‐Yuan Wu, Chia-Wei Huang, Sen‐Sung Cheng, Wei‐Hsin Sun and Chun‐Ya Lin. Their work appears in journals such as Blood, PLoS ONE and The Science of The Total Environment.
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.