Song Li
- Molecular Biology top 10%
- Cell Biology top 2%
- Immunology and Allergy top 1%
- Physiology top 5%
- Cancer Research top 5%
- Co-authors
- Shu ChienMichael KimSuli YuanYing‐Li HuYi‐Shuan LiKuang‐Den ChenDavid D. SchlaepferTony Hunter
- Topics
- Radiomics and Machine Learning in Medical Imaging (9 papers)Head and Neck Cancer Studies (8 papers)Monoclonal and Polyclonal Antibodies Research (8 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Song Li
137 papers receiving 2.7k citations
Peers
Comparison fields: 5 of 159
- Molecular Biology 1.1k
- Cell Biology 557
- Immunology and Allergy 435
- Physiology 403
- Cancer Research 314
Countries citing papers authored by Song Li
This map shows the geographic impact of Song Li'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 Song Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Song Li more than expected).
Fields of papers citing papers by Song Li
This network shows the impact of papers produced by Song Li. 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 Song Li. The network helps show where Song Li may publish in the future.
Co-authorship network of co-authors of Song Li
This figure shows the co-authorship network connecting the top 25 collaborators of Song Li. A scholar is included among the top collaborators of Song Li 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 Song Li. Song Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 5 | |
| 5 | 2 | |
| 6 | 13 | |
| 7 | 5 | |
| 8 | 12 | |
| 9 | 11 | |
| 10 | 2 | |
| 11 | 6 | |
| 12 | 28 | |
| 13 | 6 | |
| 14 | 88 | |
| 15 | 36 | |
| 16 | pnd yu he ji dui mei guo xing li shu gan fu bing shang kou de yu he xiao guo | 1 |
| 17 | Status and preventive measures of HIV mother-to-fetus transmission in Guangxi Zhuang Autonomous Region | 1 |
| 18 | Genetic Diagnosis of Spinal Muscular Atrophy Using MLPA | 1 |
| 19 | 44 | |
| 20 | [Common mutation analysis for patients found in Tianjin area with glucose-6-phosphate dehydrogenase deficiency]. | 1 |
About Song Li
Song Li is a scholar working on Otorhinolaryngology, Immunology and Allergy and Radiology, Nuclear Medicine and Imaging, having authored 147 papers that have together received 2.7k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (9 papers), Head and Neck Cancer Studies (8 papers) and Monoclonal and Polyclonal Antibodies Research (8 papers). The work is most often cited by research in Immunology and Allergy (435 citations), Cell Biology (557 citations) and Cancer Research (314 citations). Song Li has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Shu Chien, Michael Kim, Suli Yuan, Ying‐Li Hu, Yi‐Shuan Li, Kuang‐Den Chen, David D. Schlaepfer, Tony Hunter, Shila Jalali and Shunichi Usami. Their work appears in journals such as Proceedings of the National Academy of Sciences, The Lancet and Journal of Biological Chemistry.
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.