Dai Feng

775 total citations
41 papers, 442 citations indexed

About

Dai Feng is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Dai Feng has authored 41 papers receiving a total of 442 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Statistics and Probability, 10 papers in Artificial Intelligence and 6 papers in Molecular Biology. Recurrent topics in Dai Feng's work include Statistical Methods and Inference (11 papers), Bayesian Methods and Mixture Models (7 papers) and Statistical Methods in Clinical Trials (6 papers). Dai Feng is often cited by papers focused on Statistical Methods and Inference (11 papers), Bayesian Methods and Mixture Models (7 papers) and Statistical Methods in Clinical Trials (6 papers). Dai Feng collaborates with scholars based in United States, China and Canada. Dai Feng's co-authors include Lili Zhao, Richard Baumgartner, Vladimir Svetnik, Giuliana Cortese, Luke Tierney, Daniel E. Weeks, Subba Rao Indugula, Karolina A. Åberg, Satupaitea Viali and Ranjan Deka and has published in prestigious journals such as Journal of Clinical Oncology, Journal of the American Statistical Association and PLoS ONE.

In The Last Decade

Dai Feng

39 papers receiving 434 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Dai Feng United States 13 84 73 58 57 55 41 442
J. Kenneth Tay United States 6 69 0.8× 26 0.4× 69 1.2× 59 1.0× 48 0.9× 6 456
Jacqueline Corrigan‐Curay United States 9 135 1.6× 106 1.5× 23 0.4× 119 2.1× 32 0.6× 13 616
Pankaj K. Choudhary United States 15 145 1.7× 140 1.9× 86 1.5× 33 0.6× 31 0.6× 59 701
Jichun Xie United States 15 97 1.2× 56 0.8× 82 1.4× 35 0.6× 31 0.6× 39 500
Vivek A. Rudrapatna United States 11 151 1.8× 22 0.3× 30 0.5× 48 0.8× 43 0.8× 33 449
Masao Ueki Japan 13 189 2.3× 59 0.8× 65 1.1× 21 0.4× 92 1.7× 45 569
David Tritchler Canada 12 279 3.3× 56 0.8× 35 0.6× 71 1.2× 42 0.8× 27 649
Patrick K. Kimes United States 7 207 2.5× 17 0.2× 40 0.7× 50 0.9× 31 0.6× 10 507

Countries citing papers authored by Dai Feng

Since Specialization
Citations

This map shows the geographic impact of Dai Feng'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 Dai Feng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dai Feng more than expected).

Fields of papers citing papers by Dai Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dai Feng. 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 Dai Feng. The network helps show where Dai Feng may publish in the future.

Co-authorship network of co-authors of Dai Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Dai Feng. A scholar is included among the top collaborators of Dai Feng 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 Dai Feng. Dai Feng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yang, Zhaoyu, Tao Tang, Pengfei Li, et al.. (2022). Systematic analysis of tRNA-derived small RNAs reveals therapeutic targets of Xuefu Zhuyu decoction in the cortexes of experimental traumatic brain injury. Phytomedicine. 102. 154168–154168. 20 indexed citations
2.
Gossec, Laure, Dafna D. Gladman, Philipp Sewerin, et al.. (2021). AB0550 EFFICACY OF UPADACITINIB IN PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS AND A LOW OR HIGH SWOLLEN JOINT COUNT: A SUBGROUP ANALYSIS OF 2 PHASE 3 STUDIES (SELECT-PsA 1 AND SELECT-PsA 2). Annals of the Rheumatic Diseases. 80. 1308–1309. 1 indexed citations
3.
Huang, Peng, Huijun Liao, Zhenzhou Li, et al.. (2021). Elevation of plasma tRNA fragments as a promising biomarker for liver fibrosis in nonalcoholic fatty liver disease. Scientific Reports. 11(1). 5886–5886. 23 indexed citations
4.
Baumgartner, Richard, et al.. (2020). Bayesian credible subgroup identification for treatment effectiveness in time-to-event data. PLoS ONE. 15(2). e0229336–e0229336. 3 indexed citations
5.
Feng, Dai, Richard Baumgartner, & Vladimir Svetnik. (2018). A Bayesian Framework for Estimating the Concordance Correlation Coefficient Using Skew-elliptical Distributions. The International Journal of Biostatistics. 14(1). 1 indexed citations
6.
Feng, Dai, et al.. (2018). Experimental research into the potential therapeutic effect of GYY4137 on Ovariectomy-induced osteoporosis. Cellular & Molecular Biology Letters. 23(1). 47–47. 13 indexed citations
7.
Baumgartner, Richard, Aniket Joshi, Dai Feng, Francesca Zanderigo, & R. Todd Ogden. (2018). Statistical evaluation of test-retest studies in PET brain imaging. EJNMMI Research. 8(1). 13–13. 22 indexed citations
8.
Powles, Thomas, Jürgen E. Gschwend, Yohann Loriot, et al.. (2017). Pembrolizumab ± chemotherapy versus chemotherapy in advanced urothelial cancer: Phase 3 KEYNOTE-361 trial. Annals of Oncology. 28. v326–v326. 2 indexed citations
9.
Zhao, Lili, Wei-Sheng Wu, Dai Feng, Hui Jiang, & XuanLong Nguyen. (2017). Bayesian Analysis of RNA-Seq Data Using a Family of Negative Binomial Models. Bayesian Analysis. 13(2). 411–436. 5 indexed citations
10.
11.
12.
Zhao, Lili, Dai Feng, Guoan Chen, & Jeremy M. G. Taylor. (2015). A Unified Bayesian Semiparametric Approach to Assess Discrimination Ability in Survival Analysis. Biometrics. 72(2). 554–562. 5 indexed citations
13.
Feng, Dai, Richard Baumgartner, & Vladimir Svetnik. (2015). A Bayesian estimate of the concordance correlation coefficient with skewed data. Pharmaceutical Statistics. 14(4). 350–358. 2 indexed citations
14.
Feng, Dai, Richard Baumgartner, & Vladimir Svetnik. (2014). A Robust Bayesian Estimate of the Concordance Correlation Coefficient. Journal of Biopharmaceutical Statistics. 25(3). 490–507. 12 indexed citations
15.
Åberg, Karolina A., Dai Feng, Guangyun Sun, et al.. (2009). Susceptibility Loci for Adiposity Phenotypes on 8p, 9p, and 16q in American Samoa and Samoa. Obesity. 17(3). 518–524. 23 indexed citations
16.
Åberg, Karolina A., Dai Feng, Satupaitea Viali, et al.. (2009). Suggestive linkage detected for blood pressure related traits on 2q and 22q in the population on the Samoan islands. BMC Medical Genetics. 10(1). 107–107. 8 indexed citations
17.
Feng, Dai, et al.. (2009). Adaptive Optimization Control Based on Improved Genetic Algorithm and Fuzzy Neural Network. 17. 1–4. 1 indexed citations
18.
Åberg, Karolina A., Dai Feng, Guangyun Sun, et al.. (2008). A genome-wide linkage scan identifies multiple chromosomal regions influencing serum lipid levels in the population on the Samoan islands. Journal of Lipid Research. 49(10). 2169–2178. 26 indexed citations
19.
Feng, Dai, Guoqiang Sun, Karolina A. Åberg, et al.. (2008). A Whole Genome Linkage Scan Identifies Multiple Chromosomal Regions Influencing Adiposity‐Related Traits among Samoans. Annals of Human Genetics. 72(6). 780–792. 26 indexed citations
20.
Feng, Dai & Daniel E. Weeks. (2006). Ordered Genotypes: An Extended ITO Method and a General Formula for Genetic Covariance. The American Journal of Human Genetics. 78(6). 1035–1045. 4 indexed citations

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.

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