Shengchun Kong
- Oncology top 10%
- Pulmonary and Respiratory Medicine top 10%
- Immunology
- Statistics and Probability top 5%
- Molecular Biology
- Co-authors
- Bin NanNaoyuki NogamiDenis Moro‐SibilotGene Grant FinleyShelley ColemanFabrice BarlésiMark L. McClelandWei Zou
- Topics
- Statistical Methods and Inference (6 papers)Hormonal and reproductive studies (5 papers)Statistical Methods and Bayesian Inference (4 papers)
- Journals
- Journal of the American Statistical AssociationThe Journal of Clinical Endocrinology & MetabolismBiometrika
- Partner nations
- United StatesChileSpain
In The Last Decade
Shengchun Kong
15 papers receiving 516 citations
Hit Papers
Peers
Comparison fields: 5 of 65
- Oncology 312
- Pulmonary and Respiratory Medicine 227
- Immunology 72
- Statistics and Probability 72
- Molecular Biology 60
Countries citing papers authored by Shengchun Kong
This map shows the geographic impact of Shengchun Kong'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 Shengchun Kong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shengchun Kong more than expected).
Fields of papers citing papers by Shengchun Kong
This network shows the impact of papers produced by Shengchun Kong. 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 Shengchun Kong. The network helps show where Shengchun Kong may publish in the future.
Co-authorship network of co-authors of Shengchun Kong
This figure shows the co-authorship network connecting the top 25 collaborators of Shengchun Kong. A scholar is included among the top collaborators of Shengchun Kong 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 Shengchun Kong. Shengchun Kong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 12 | |
| 2 | IMpower150 Final Overall Survival Analyses for Atezolizumab Plus Bevacizumab and Chemotherapy in First-Line Metastatic Nonsquamous NSCLCbreakdown → | 316 |
| 3 | 7 | |
| 4 | 4 | |
| 5 | 21 | |
| 6 | 6 | |
| 7 | 21 | |
| 8 | 16 | |
| 9 | 23 | |
| 10 | 26 | |
| 11 | 2 | |
| 12 | 1 | |
| 13 | 5 | |
| 14 | 35 | |
| 15 | 27 |
About Shengchun Kong
Shengchun Kong is a scholar working on Statistics and Probability, Endocrinology, Diabetes and Metabolism and Reproductive Medicine, having authored 15 papers that have together received 522 indexed citations. Recurring topics across this work include Statistical Methods and Inference (6 papers), Hormonal and reproductive studies (5 papers) and Statistical Methods and Bayesian Inference (4 papers). The work is most often cited by research in Oncology (312 citations), Statistics and Probability (72 citations) and Pulmonary and Respiratory Medicine (227 citations). Shengchun Kong has collaborated with scholars based in United States, Chile and Spain. Frequent co-authors include Bin Nan, Naoyuki Nogami, Denis Moro‐Sibilot, Gene Grant Finley, Shelley Coleman, Fabrice Barlési, Bin Nan, Mark L. McCleland, Wei Zou and Mark A. Socinski. Their work appears in journals such as Journal of the American Statistical Association, The Journal of Clinical Endocrinology & Metabolism and Biometrika.
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.