Wei Pan

14.6k total citations · 3 hit papers
273 papers, 9.8k citations indexed

About

Wei Pan is a scholar working on Molecular Biology, Genetics and Statistics and Probability. According to data from OpenAlex, Wei Pan has authored 273 papers receiving a total of 9.8k indexed citations (citations by other indexed papers that have themselves been cited), including 136 papers in Molecular Biology, 121 papers in Genetics and 67 papers in Statistics and Probability. Recurrent topics in Wei Pan's work include Genetic Associations and Epidemiology (94 papers), Gene expression and cancer classification (73 papers) and Bioinformatics and Genomic Networks (72 papers). Wei Pan is often cited by papers focused on Genetic Associations and Epidemiology (94 papers), Gene expression and cancer classification (73 papers) and Bioinformatics and Genomic Networks (72 papers). Wei Pan collaborates with scholars based in United States, China and Hong Kong. Wei Pan's co-authors include Xiaotong Shen, Peng Wei, Han Fang, Jizhen Lin, Chap T. Le, Chong Wu, Haoran Xue, Yunzhang Zhu, Saonli Basu and Yangqing Deng and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Journal of the American Statistical Association.

In The Last Decade

Wei Pan

271 papers receiving 9.4k citations

Hit Papers

Akaike's Information Criterion in Generalized Estimating ... 2001 2026 2009 2017 2001 2021 2021 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wei Pan United States 47 3.5k 2.4k 1.7k 967 514 273 9.8k
Brian S. Yandell United States 49 2.8k 0.8× 2.4k 1.0× 1.1k 0.7× 639 0.7× 257 0.5× 153 9.8k
Anne‐Laure Boulesteix Germany 47 2.6k 0.7× 747 0.3× 916 0.5× 1.8k 1.9× 1.2k 2.3× 148 12.0k
Kathryn Roeder United States 54 3.5k 1.0× 5.7k 2.4× 1.4k 0.8× 968 1.0× 165 0.3× 164 13.5k
Geert Verbeke Belgium 50 1.2k 0.3× 1.3k 0.5× 4.3k 2.5× 1.3k 1.3× 293 0.6× 312 14.2k
Rob Tibshirani United States 19 4.7k 1.3× 1.2k 0.5× 1.4k 0.8× 1.2k 1.3× 369 0.7× 28 14.2k
Yudi Pawitan Sweden 52 5.4k 1.6× 3.0k 1.3× 1.2k 0.7× 575 0.6× 174 0.3× 231 14.3k
Sandrine Dudoit United States 50 11.4k 3.3× 1.6k 0.7× 1.5k 0.9× 1.7k 1.7× 323 0.6× 99 17.4k
Russell D. Wolfinger United States 39 2.7k 0.8× 1.4k 0.6× 903 0.5× 178 0.2× 953 1.9× 89 9.5k
Chris Holmes United Kingdom 42 1.6k 0.5× 972 0.4× 1.2k 0.7× 1.8k 1.9× 90 0.2× 146 6.5k
Frank Bretz Switzerland 40 1.6k 0.4× 1.3k 0.5× 3.9k 2.3× 489 0.5× 3.1k 6.0× 185 17.3k

Countries citing papers authored by Wei Pan

Since Specialization
Citations

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

Fields of papers citing papers by Wei Pan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wei Pan

This figure shows the co-authorship network connecting the top 25 collaborators of Wei Pan. A scholar is included among the top collaborators of Wei Pan 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 Wei Pan. Wei Pan 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.
Pan, Wei, et al.. (2025). Immune Cells and Intracerebral Hemorrhage: A Causal Investigation Through Mendelian Randomization. Brain and Behavior. 15(1). e70263–e70263. 4 indexed citations
2.
Liao, Hui‐ling, Haoran Xue, & Wei Pan. (2024). Inferring causal direction between two traits using R2 with application to transcriptome-wide association studies. The American Journal of Human Genetics. 111(8). 1782–1795. 1 indexed citations
3.
Wang, Kui, Jiawei Wang, Yuhua Chen, et al.. (2024). Causal relationship between gut microbiota and risk of esophageal cancer: evidence from Mendelian randomization study. Aging. 16(4). 3596–3611. 11 indexed citations
4.
Shen, Xiaotong, et al.. (2024). Inferring a directed acyclic graph of phenotypes from GWAS summary statistics. Biometrics. 80(1). 2 indexed citations
5.
Pan, Wei, et al.. (2023). Statistical inference with large‐scale trait imputation. Statistics in Medicine. 43(4). 625–641. 1 indexed citations
6.
Lin, Zhaotong, et al.. (2023). Integrating GWAS summary statistics, individual-level genotypic and omic data to enhance the performance for large-scale trait imputation. Human Molecular Genetics. 32(17). 2693–2703. 1 indexed citations
7.
Lin, Zhaotong, et al.. (2023). DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies. Biostatistics. 25(2). 468–485. 6 indexed citations
8.
Lin, Zhaotong, et al.. (2023). Inference of causal metabolite networks in the presence of invalid instrumental variables with GWAS summary data. Genetic Epidemiology. 47(8). 585–599. 1 indexed citations
9.
Wu, Chong, Jingjing Zhu, Xiaoran Tong, et al.. (2021). Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data: a multi‐phased study of prostate cancer. Cancer Communications. 41(12). 1387–1397. 8 indexed citations
10.
Hebbel, Robert P., Peng Wei, Liming Milbauer, et al.. (2020). Abnormal Endothelial Gene Expression Associated With Early Coronary Atherosclerosis. Journal of the American Heart Association. 9(14). e016134–e016134. 23 indexed citations
11.
Zhang, Jianfeng, Zhaopeng Luo, Jingjing Jin, et al.. (2017). Genetic diversities of 24 tobacco cultivars analyzed by SNP. Tobacco Science & Technology. 2 indexed citations
12.
Kwak, Il‐Youp & Wei Pan. (2016). Gene- and pathway-based association tests for multiple traits with GWAS summary statistics. Bioinformatics. 33(1). 64–71. 31 indexed citations
13.
Pan, Wei, Il‐Youp Kwak, & Peng Wei. (2015). A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants. The American Journal of Human Genetics. 97(1). 86–98. 48 indexed citations
14.
Kim, Junghi, Jeffrey R. Wozniak, Bryon A. Mueller, & Wei Pan. (2014). Testing Group Differences in Brain Functional Connectivity: Using Correlations or Partial Correlations?. Brain Connectivity. 5(4). 214–231. 23 indexed citations
15.
Basu, Saonli, Wei Pan, & William S. Oetting. (2011). A Dimension Reduction Approach for Modeling Multi-Locus Interaction in Case-Control Studies. Human Heredity. 71(4). 234–245. 6 indexed citations
16.
Pan, Wei. (2009). Statistical Tests of Genetic Association in the Presence of Gene-Gene and Gene-Environment Interactions. Human Heredity. 69(2). 131–142. 10 indexed citations
17.
Parker, David L., et al.. (2007). Organizational Characteristics of Small Metal-Fabricating Businessesin Minnesota. International Journal of Occupational and Environmental Health. 13(2). 160–166. 6 indexed citations
18.
Guo, Xu, Huilin Qi, Catherine M. Verfaillie, & Wei Pan. (2003). Statistical significance analysis of longitudinal gene expression data. Bioinformatics. 19(13). 1628–1635. 30 indexed citations
19.
Pan, Wei & John E. Connett. (2002). SELECTING THE WORKING CORRELATION STRUCTURE IN GENERALIZED ESTIMATING EQUATIONS WITH APPLICATION TO THE LUNG HEALTH STUDY. Statistica Sinica. 12(2). 475–490. 61 indexed citations
20.
Pan, Wei. (1999). Extending the Iterative Convex Minorant Algorithm to the Cox Model for Interval-Censored Data. Journal of Computational and Graphical Statistics. 8(1). 109–120. 103 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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