Lu Tang
Impact in
- Cancer Research top 2%
- Cancer, Hypoxia, and Metabolism
- Cancer-related molecular mechanisms research
- MicroRNA in disease regulation
Papers in
-
- Circular RNAs in diseases 8
- RNA modifications and cancer 7
- Epidemiology 20
- Substance Abuse Treatment and Outcomes 6
- Co-authors
- Heng Mei (9 shared papers)Yu Hu (9 shared papers)Shan Rao (7 shared papers)Yujuan Zhou (7 shared papers)Pin Yi (7 shared papers)Linda Oyang (7 shared papers)Qianjin Liao (7 shared papers)Jinguan Lin (6 shared papers)
- Journals
- Journal of Cancer (4 papers)PLoS ONE (3 papers)Scientific Reports (2 papers)Frontiers in Immunology (2 papers)OncoTargets and Therapy (2 papers)
- Partner nations
- ChinaUnited StatesMexico
In The Last Decade
Lu Tang
102 papers receiving 3.3k citations
Lu Tang's Hit Papers
Peers
Comparison fields: 5 of 163
- Cancer Research 728
- Critical Care and Intensive Care Medicine 145
- Immunology 520
- Oncology 636
- Modeling and Simulation 106
Countries citing papers authored by Lu Tang
This map shows the geographic impact of Lu Tang'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 Lu Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lu Tang more than expected).
Fields of papers citing papers by Lu Tang
This network shows the impact of papers produced by Lu Tang. 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 Lu Tang. The network helps show where Lu Tang may publish in the future.
Co-authors
The 25 scholars most cited alongside Lu Tang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 107 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | The cancer metabolic reprogramming and immune response Hit paper breakdown → | 2021 | 799 |
| 2 | 2020 | 328 | |
| 3 | 2020 | 144 | |
| 4 | 2019 | 95 | |
| 5 | 2014 | 93 | |
| 6 | 2020 | 90 | |
| 7 | 2020 | 88 | |
| 8 | 2012 | 78 | |
| 9 | 2023 | 77 | |
| 10 | 2021 | 71 | |
| 11 | 2019 | 67 | |
| 12 | 2014 | 65 | |
| 13 | 2014 | 57 | |
| 14 | 2014 | 51 | |
| 15 | 2012 | 47 | |
| 16 | 2022 | 42 | |
| 17 | 2016 | 42 | |
| 18 | Fused Lasso Approach in Regression Coefficients Clustering - Learning Parameter Heterogeneity in Data Integration. | 2016 | 42 |
| 19 | 2017 | 39 | |
| 20 | 2014 | 39 |
About Lu Tang
Lu Tang is a scholar working on Molecular Biology, Epidemiology, Cancer Research, Artificial Intelligence and Pulmonary and Respiratory Medicine, having authored 107 papers that have together received 3.4k indexed citations. Recurring topics across this work include MicroRNA in disease regulation (9 papers), Cancer-related molecular mechanisms research (9 papers), Circular RNAs in diseases (8 papers), RNA modifications and cancer (7 papers), Opioid Use Disorder Treatment (6 papers), Diet and metabolism studies (6 papers), Substance Abuse Treatment and Outcomes (6 papers) and Statistical Methods and Inference (5 papers). The work is most often cited by research in Cancer Research (728 citations), Critical Care and Intensive Care Medicine (145 citations), Immunology (520 citations), Oncology (636 citations) and Modeling and Simulation (106 citations). Lu Tang has collaborated with scholars based in China, United States and Mexico. Frequent co-authors include Heng Mei, Yu Hu, Shan Rao, Yujuan Zhou, Pin Yi, Linda Oyang, Qianjin Liao, Jinguan Lin, Jiaxin Liang and Shiming Tan. Their work appears in journals such as Journal of Cancer, PLoS ONE, Scientific Reports, Frontiers in Immunology and OncoTargets and Therapy.
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.