Ren Qi
Impact in
- Biophysics top 5%
- Cell Image Analysis Techniques
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- Cancer-related molecular mechanisms research
- MicroRNA in disease regulation
Papers in
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- Single-cell and spatial transcriptomics 8
- Gene expression and cancer classification 5
- Machine Learning in Bioinformatics 2
- Bioinformatics and Genomic Networks 2
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- Cancer-related molecular mechanisms research 4
- MicroRNA in disease regulation 1
- Co-authors
- Quan Zou (5 shared papers)Qin Ma (3 shared papers)Anjun Ma (3 shared papers)Cankun Wang (2 shared papers)Hongjun Fu (2 shared papers)Dong Xu (1 shared paper)Juexin Wang (1 shared paper)Yuexu Jiang (1 shared paper)
- Journals
- Briefings in Bioinformatics (3 papers)IEEE/ACM Transactions on Computational Biology and Bioinformatics (2 papers)Computers in Biology and Medicine (1 paper)iScience (1 paper)Nature Communications (1 paper)
- Partner nations
- ChinaUnited StatesSaudi Arabia
In The Last Decade
Ren Qi
13 papers receiving 634 citations
Ren Qi's Hit Papers
Peers
Comparison fields: 5 of 78
- Biophysics 101
- Cancer Research 132
- Molecular Biology 527
- Neurology 47
- Artificial Intelligence 76
Countries citing papers authored by Ren Qi
This map shows the geographic impact of Ren Qi'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 Ren Qi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ren Qi more than expected).
Fields of papers citing papers by Ren Qi
This network shows the impact of papers produced by Ren Qi. 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 Ren Qi. The network helps show where Ren Qi may publish in the future.
Co-authors
The 25 scholars most cited alongside Ren Qi, 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 | scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses Hit paper breakdown → | 2021 | 250 |
| 2 | 2019 | 141 | |
| 3 | 2020 | 71 | |
| 4 | 2020 | 50 | |
| 5 | 2023 | 50 | |
| 6 | 2020 | 38 | |
| 7 | 2022 | 18 | |
| 8 | 2018 | 10 | |
| 9 | 2022 | 6 | |
| 10 | 2024 | 1 | |
| 11 | Multiple Kernel Geometric Mean Metric Learning for Heterogeneous Data | 2017 | 1 |
| 12 | 2024 | 1 | |
| 13 | 2025 | 1 | |
| 14 | 2025 | 0 |
About Ren Qi
Ren Qi is a scholar working on Molecular Biology, Cancer Research, Computer Vision and Pattern Recognition, Artificial Intelligence and Immunology, having authored 14 papers that have together received 638 indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (8 papers), Gene expression and cancer classification (5 papers), Cancer-related molecular mechanisms research (4 papers), Face and Expression Recognition (3 papers), Machine Learning in Bioinformatics (2 papers), Bioinformatics and Genomic Networks (2 papers), Immune cells in cancer (2 papers) and MicroRNA in disease regulation (1 paper). The work is most often cited by research in Biophysics (101 citations), Cancer Research (132 citations), Molecular Biology (527 citations), Neurology (47 citations) and Artificial Intelligence (76 citations). Ren Qi has collaborated with scholars based in China, United States and Saudi Arabia. Frequent co-authors include Quan Zou, Qin Ma, Anjun Ma, Cankun Wang, Hongjun Fu, Dong Xu, Juexin Wang, Yuexu Jiang, Yuzhou Chang and Jianting Gong. Their work appears in journals such as Briefings in Bioinformatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Computers in Biology and Medicine, iScience and Nature Communications.
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.