Yiran Cai
- Molecular Biology
- Cancer Research top 10%
- Oncology
- Pulmonary and Respiratory Medicine
- Computer Vision and Pattern Recognition
- Topics
- Lung Cancer Treatments and Mutations (7 papers)Cancer Genomics and Diagnostics (5 papers)Cancer Immunotherapy and Biomarkers (3 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Yiran Cai
18 papers receiving 498 citations
Hit Papers
Peers
Comparison fields: 5 of 74
- Molecular Biology 330
- Cancer Research 245
- Oncology 111
- Pulmonary and Respiratory Medicine 76
- Computer Vision and Pattern Recognition 33
Countries citing papers authored by Yiran Cai
This map shows the geographic impact of Yiran Cai'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 Yiran Cai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yiran Cai more than expected).
Fields of papers citing papers by Yiran Cai
This network shows the impact of papers produced by Yiran Cai. 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 Yiran Cai. The network helps show where Yiran Cai may publish in the future.
Co-authorship network of co-authors of Yiran Cai
This figure shows the co-authorship network connecting the top 25 collaborators of Yiran Cai. A scholar is included among the top collaborators of Yiran Cai 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 Yiran Cai. Yiran Cai 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 | 5 | |
| 3 | 2 | |
| 4 | Projected cross-view learning for unbalanced incomplete multi-view clusteringbreakdown → | 53 |
| 5 | 3 | |
| 6 | 15 | |
| 7 | 0 | |
| 8 | 22 | |
| 9 | 12 | |
| 10 | 5 | |
| 11 | 24 | |
| 12 | 2 | |
| 13 | 4 | |
| 14 | 3 | |
| 15 | [Clinicopathological features of lung adenocarcinoma harboring anaplastic lymphoma kinase rearrangements]. | 1 |
| 16 | 91 | |
| 17 | [Primary sinus histiocytosis of the trachea: a case report and review of literature]. | 4 |
| 18 | 1 | |
| 19 | 258 | |
| 20 | [Pathologic diagnosis of endometrial carcinoma in curettage specimens in women under forty years of age]. | 1 |
About Yiran Cai
Yiran Cai is a scholar working on Cancer Research, Computer Graphics and Computer-Aided Design and Pulmonary and Respiratory Medicine, having authored 20 papers that have together received 506 indexed citations. Recurring topics across this work include Lung Cancer Treatments and Mutations (7 papers), Cancer Genomics and Diagnostics (5 papers) and Cancer Immunotherapy and Biomarkers (3 papers). The work is most often cited by research in Cancer Research (245 citations), Computational Mathematics (8 citations) and Molecular Biology (330 citations). Yiran Cai has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Zhihua Liu, Yanyan Tian, Fang Ding, Hongyan Chen, Qin Su, Aiping Luo, Cheng Liu, Man-Fai Leung, Hangjun Che and Shiping Wen. Their work appears in journals such as Journal of Biological Chemistry, Journal of Clinical Oncology and Neural Networks.
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.