Xin Yan
Impact in
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- Computational Drug Discovery Methods
- Artificial Intelligence top 5%
Papers in
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- Face and Expression Recognition 8
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- Imbalanced Data Classification Techniques 5
- Co-authors
- Xiao SuXiaogang SuChih‐Ling TsaiLei WangZhu‐Hong YouJian LuoShixiong XiaYong Zhou
- Journals
- Scientific Reports (5 papers)Soft Computing (3 papers)Sustainability (2 papers)Journal of Theoretical Biology (2 papers)Journal of the American Society for Mass Spectrometry (1 paper)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Xin Yan
52 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 190
- Computational Theory and Mathematics 318
- Artificial Intelligence 335
- Cancer Research 140
- Molecular Biology 583
- Statistics and Probability 56
Countries citing papers authored by Xin Yan
This map shows the geographic impact of Xin Yan'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 Xin Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xin Yan more than expected).
Fields of papers citing papers by Xin Yan
This network shows the impact of papers produced by Xin Yan. 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 Xin Yan. The network helps show where Xin Yan may publish in the future.
Co-authors
The 25 scholars most cited alongside Xin Yan, 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 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 6 | |
| 5 | 2022 | 24 | |
| 6 | 2022 | 14 | |
| 7 | 2021 | 21 | |
| 8 | 2021 | 43 | |
| 9 | 2021 | 2 | |
| 10 | 2020 | 16 | |
| 11 | 2020 | 4 | |
| 12 | 2019 | 3 | |
| 13 | 2019 | 65 | |
| 14 | 2018 | 34 | |
| 15 | 2018 | 23 | |
| 16 | 2017 | 33 | |
| 17 | 2016 | 22 | |
| 18 | Discriminant Analysis Using Multigene Expression Profiles in Classification of Breast Cancer. | 2007 | 2 |
| 19 | Document generality: its computation for ranking | 2006 | 4 |
| 20 | 2004 | 6 |
About Xin Yan
Xin Yan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Statistics and Probability, Cancer Research and Computational Theory and Mathematics, having authored 57 papers that have together received 1.7k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (13 papers), Bioinformatics and Genomic Networks (11 papers), Face and Expression Recognition (8 papers), Cancer-related molecular mechanisms research (6 papers), Computational Drug Discovery Methods (6 papers), MicroRNA in disease regulation (5 papers), Imbalanced Data Classification Techniques (5 papers) and Circular RNAs in diseases (4 papers). The work is most often cited by research in Computational Theory and Mathematics (318 citations), Artificial Intelligence (335 citations), Cancer Research (140 citations), Molecular Biology (583 citations) and Statistics and Probability (56 citations). Xin Yan has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Xiao Su, Xiaogang Su, Chih‐Ling Tsai, Lei Wang, Zhu‐Hong You, Jian Luo, Shixiong Xia, Yong Zhou, Ye Tian and Xing Chen. Their work appears in journals such as Scientific Reports, Soft Computing, Sustainability, Journal of Theoretical Biology and Journal of the American Society for Mass Spectrometry.
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.