Leying Guan

1.4k total citations
20 papers, 158 citations indexed

About

Leying Guan is a scholar working on Molecular Biology, Statistics and Probability and Artificial Intelligence. According to data from OpenAlex, Leying Guan has authored 20 papers receiving a total of 158 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 7 papers in Statistics and Probability and 5 papers in Artificial Intelligence. Recurrent topics in Leying Guan's work include Statistical Methods and Inference (6 papers), Gene expression and cancer classification (4 papers) and Sparse and Compressive Sensing Techniques (2 papers). Leying Guan is often cited by papers focused on Statistical Methods and Inference (6 papers), Gene expression and cancer classification (4 papers) and Sparse and Compressive Sensing Techniques (2 papers). Leying Guan collaborates with scholars based in United States, China and Sweden. Leying Guan's co-authors include Robert Tibshirani, Steven H. Kleinstein, Jeremy P. Gygi, Saurabh Gombar, Zhou Fan, Robert A. H. Scott, Xiaoying Tian, Gomathi Krishnan, Balasubramanian Narasimhan and Tho D. Pham 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

Leying Guan

17 papers receiving 155 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Leying Guan United States	7	39	28	25	22	15	20	158
David Martínez-Enguita Sweden	4	29 0.7×	6 0.2×	60 2.4×		2 0.1×	7	296
A. Kellner Germany	8	109 2.8×	14 0.5×	53 2.1×	36 1.6×		22	364
Shukai Li China	7	25 0.6×	2 0.1×	28 1.1×	1 0.0×	5 0.3×	30	236
Pallab Roy Australia	10	17 0.4×	50 1.8×	10 0.4×	53 2.4×		20	280
Sakyajit Bhattacharya India	11	108 2.8×	3 0.1×	15 0.6×		10 0.7×	25	286
M. Meena India	9	10 0.3×	27 1.0×	4 0.2×	7 0.3×		35	256
Mathew W. McLean United States	6	39 1.0×		22 0.9×	1 0.0×	80 5.3×	7	215
Jigar Kadakia United States	10	9 0.2×	2 0.1×	22 0.9×	20 0.9×		20	322
Miew Keen Choong Australia	6	49 1.3×		58 2.3×	1 0.0×	6 0.4×	17	220

Countries citing papers authored by Leying Guan

Since Specialization

Citations

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

Fields of papers citing papers by Leying Guan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Leying Guan

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

All Works

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20 of 20 papers shown

Zhou, Geyu, et al.. (2025). JointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation. Nature Communications. 16(1). 3841–3841. 1 indexed citations

Greene, Kerrie, Alexandra Tabachnikova, Bornali Bhattacharjee, et al.. (2025). Cerebrospinal fluid immune phenotyping reveals distinct immunotypes of myalgic encephalomyelitis/chronic fatigue syndrome. The Journal of Immunology. 214(7). 1539–1551. 2 indexed citations

Guan, Leying. (2024). A conformal test of linear models via permutation-augmented regressions. The Annals of Statistics. 52(5).

Gygi, Jeremy P., Anna Konstorum, Shrikant Pawar, et al.. (2024). A supervised Bayesian factor model for the identification of multi-omics signatures. Bioinformatics. 40(5).

Guan, Leying, et al.. (2024). Pan-cancer analysis of the potential of PEA3 subfamily genes as tumor markers. Scientific Reports. 14(1). 31518–31518. 1 indexed citations

Willemsen, Lisa, Joaquin Reyna, Brendan Ha, et al.. (2024). A systems vaccinology resource to develop and test computational models of immunity. The Journal of Immunology. 212(1_Supplement). 1571_5604–1571_5604.

Gygi, Jeremy P., Steven H. Kleinstein, & Leying Guan. (2023). Predictive overfitting in immunological applications: Pitfalls and solutions. Human Vaccines & Immunotherapeutics. 19(2). 2251830–2251830. 25 indexed citations

Guan, Leying. (2023). Smooth and Probabilistic PARAFAC Model with Auxiliary Covariates. Journal of Computational and Graphical Statistics. 33(2). 538–550. 2 indexed citations

Guan, Leying. (2022). Localized conformal prediction: a generalized inference framework for conformal prediction. Biometrika. 110(1). 33–50. 17 indexed citations

10.

Guan, Leying & Robert Tibshirani. (2022). Prediction and Outlier Detection in Classification Problems. Journal of the Royal Statistical Society Series B (Statistical Methodology). 84(2). 524–546. 15 indexed citations

11.

Guan, Leying & Robert Tibshirani. (2020). Post model‐fitting exploration via a “Next‐Door” analysis. Canadian Journal of Statistics. 48(3). 447–470. 4 indexed citations

12.

Guan, Leying & Rob Tibshirani. (2019). Prediction and outlier detection: a distribution-free prediction set with a balanced objective. arXiv (Cornell University). 1 indexed citations

13.

Wagar, Lisa E., Christopher R. Bolen, Natalia Sigal, et al.. (2019). Increased T Cell Differentiation and Cytolytic Function in Bangladeshi Compared to American Children. Frontiers in Immunology. 10. 2239–2239. 12 indexed citations

14.

Guan, Leying, Xi Chen, & Wing Hung Wong. (2019). Detecting Strong Signals in Gene Perturbation Experiments: An Adaptive Approach With Power Guarantee and FDR Control. Journal of the American Statistical Association. 115(532). 1747–1755. 2 indexed citations

15.

Fan, Zhou & Leying Guan. (2018). Approximate $\ell_{0}$-penalized estimation of piecewise-constant signals on graphs. The Annals of Statistics. 46(6B). 11 indexed citations

16.

Zhou, Fan & Leying Guan. (2017). $l_0$-estimation of piecewise-constant signals on graphs. arXiv (Cornell University). 1 indexed citations

17.

Guan, Leying, Fan Zhou, & Robert Tibshirani. (2017). Supervised learning via the “hubNet” procedure. Statistica Sinica. 28(3). 1225–1243. 2 indexed citations

18.

Guan, Leying, Xiaoying Tian, Saurabh Gombar, et al.. (2017). Big data modeling to predict platelet usage and minimize wastage in a tertiary care system. Proceedings of the National Academy of Sciences. 114(43). 11368–11373. 52 indexed citations

19.

Samusik, Nikolay, et al.. (2017). Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks. PLoS Computational Biology. 13(12). e1005875–e1005875. 6 indexed citations

20.

Guan, Leying, Qian Yang, Mengting Gu, Liang Chen, & Xuegong Zhang. (2014). Exon expression QTL (eeQTL) analysis highlights distant genomic variations associated with splicing regulation. Quantitative Biology. 2(2). 71–79. 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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