Ruoqing Zhu

1.5k total citations
55 papers, 621 citations indexed

About

Ruoqing Zhu is a scholar working on Molecular Biology, Statistics and Probability and Artificial Intelligence. According to data from OpenAlex, Ruoqing Zhu has authored 55 papers receiving a total of 621 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 16 papers in Statistics and Probability and 11 papers in Artificial Intelligence. Recurrent topics in Ruoqing Zhu's work include Statistical Methods and Inference (14 papers), Nutritional Studies and Diet (8 papers) and Metabolomics and Mass Spectrometry Studies (6 papers). Ruoqing Zhu is often cited by papers focused on Statistical Methods and Inference (14 papers), Nutritional Studies and Diet (8 papers) and Metabolomics and Mass Spectrometry Studies (6 papers). Ruoqing Zhu collaborates with scholars based in United States, China and Hong Kong. Ruoqing Zhu's co-authors include Michael R. Kosorok, Donglin Zeng, Yifan Cui, Shuangge Ma, Hongyu Zhao, Michael Welge, Colleen Bushell, Qing Zhao, Guanhua Chen and Stefan Wager and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and Bioinformatics.

In The Last Decade

Ruoqing Zhu

45 papers receiving 608 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ruoqing Zhu United States	14	181	150	110	55	43	55	621
Mauro Gasparini Italy	18	279 1.5×	134 0.9×	92 0.8×	91 1.7×	110 2.6×	61	1.1k
Xinyan Zhang United States	19	109 0.6×	441 2.9×	84 0.8×	19 0.3×	63 1.5×	57	992
Elisabeth Waldmann Germany	15	120 0.7×	66 0.4×	73 0.7×	64 1.2×	19 0.4×	21	763
Steffen Unkel United Kingdom	13	67 0.4×	112 0.7×	50 0.5×	34 0.6×	19 0.4×	26	673
Sandra E. Sinisi United States	8	322 1.8×	44 0.3×	66 0.6×	82 1.5×	19 0.4×	10	669
Peng Zeng United States	11	205 1.1×	69 0.5×	65 0.6×	41 0.7×	70 1.6×	41	475
Jae Min United States	10	46 0.3×	52 0.3×	134 1.2×	30 0.5×	24 0.6×	24	547
Woncheol Jang South Korea	13	85 0.5×	78 0.5×	48 0.4×	12 0.2×	24 0.6×	47	532
Yanming Li United States	11	79 0.4×	76 0.5×	48 0.4×	23 0.4×	25 0.6×	45	408
Brian D. Williamson United States	12	56 0.3×	96 0.6×	40 0.4×	24 0.4×	16 0.4×	44	501

Countries citing papers authored by Ruoqing Zhu

Since Specialization

Citations

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

Fields of papers citing papers by Ruoqing Zhu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ruoqing Zhu

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Holscher, Hannah D., et al.. (2025). Predicting Cognitive Outcome Through Nutrition and Health Markers Using Supervised Machine Learning. Journal of Nutrition. 155(7). 2144–2153. 1 indexed citations

Zhu, Ruoqing, et al.. (2024). Dietary patterns among U.S. food insecure cancer survivors and the risk of mortality: NHANES 1999–2018. Cancer Causes & Control. 35(7). 1075–1088.

Folta, Timothy B., et al.. (2024). Analyzing the online word of mouth dynamics: A novel approach. Decision Support Systems. 185. 114306–114306.

Kim, Tae‐Hyung, et al.. (2024). Prevalence, Management, and Comorbidities of Adults With Atrial Fibrillation in the United States, 2019 to 2023. JACC Advances. 3(11). 101330–101330. 7 indexed citations

Xiao, Yue, et al.. (2023). 5G Low-SAR Antenna with Spatial Reverse Current Distribution. 551–554.

Zhou, Haowen, et al.. (2022). Population analysis of mortality risk: Predictive models from passive monitors using motion sensors for 100,000 UK Biobank participants. SHILAP Revista de lepidopterología. 1(10). e0000045–e0000045. 6 indexed citations

Zhu, Ruoqing, et al.. (2022). Dimension Reduction Forests: Local Variable Importance Using Structured Random Forests. Journal of Computational and Graphical Statistics. 31(4). 1104–1113. 4 indexed citations

Di, Shuang, Jeremy Petch, Hertzel C. Gerstein, Ruoqing Zhu, & Diana Sherifali. (2022). Optimizing Health Coaching for Patients With Type 2 Diabetes Using Machine Learning: Model Development and Validation Study. JMIR Formative Research. 6(9). e37838–e37838. 6 indexed citations

Li, Kaiqiao, Zhenyu Zhang, Biwei Cao, et al.. (2021). Efficient gradient boosting for prognostic biomarker discovery. Bioinformatics. 38(6). 1631–1638. 40 indexed citations

10.

Li, Yutong, et al.. (2021). The Impact of Almond and Walnut Consumption on the Human Fecal Metabolome. Current Developments in Nutrition. 5. 1180–1180. 2 indexed citations

11.

Auvil, Loretta, Michael Welge, Colleen Bushell, et al.. (2020). Fecal Bacteria as Biomarkers for Predicting Food Intake in Healthy Adults. Journal of Nutrition. 151(2). 423–433. 25 indexed citations

12.

Zhao, Ying‐Qi, et al.. (2020). Constructing dynamic treatment regimes with shared parameters for censored data. Statistics in Medicine. 39(9). 1250–1263. 7 indexed citations

13.

Cui, Yifan, Ruoqing Zhu, Mai Zhou, & Michael R. Kosorok. (2020). Consistency of survival tree and forest models: splitting bias and correction. Statistica Sinica. 8 indexed citations

14.

Zhu, Ruoqing. (2019). Tree-based methods for survival analysis and high-dimensional data. Carolina Digital Repository (University of North Carolina at Chapel Hill). 2 indexed citations

15.

Mi, Xinlei, Fei Zou, & Ruoqing Zhu. (2018). Bagging and Deep Learning in Optimal Individualized Treatment Rules. Biometrics. 75(2). 674–684. 22 indexed citations

16.

Yi, Ming, Ruoqing Zhu, & Robert M. Stephens. (2018). GradientScanSurv—An exhaustive association test method for gene expression data with censored survival outcome. PLoS ONE. 13(12). e0207590–e0207590. 3 indexed citations

17.

Cui, Yifan, Ruoqing Zhu, & Michael R. Kosorok. (2017). Tree based weighted learning for estimating individualized treatment rules with censored data. Electronic Journal of Statistics. 11(2). 3927–3953. 26 indexed citations

18.

Zhu, Ruoqing, Qing Zhao, Hongyu Zhao, & Shuangge Ma. (2016). Integrating multidimensional omics data for cancer outcome. Biostatistics. 17(4). 605–618. 30 indexed citations

19.

Zhu, Ruoqing, О. Г. Шевченко, Cathleen Ma, et al.. (2013). Poplars with a PtDDM1-RNAi transgene have reduced DNA methylation and show aberrant post-dormancy morphology. Planta. 237(6). 1483–1493. 19 indexed citations

20.

Zhu, Ruoqing & Michael R. Kosorok. (2012). Recursively Imputed Survival Trees. Journal of the American Statistical Association. 107(497). 331–340. 44 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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