Mayetri Gupta

727 total citations
24 papers, 457 citations indexed

About

Mayetri Gupta is a scholar working on Molecular Biology, Genetics and Artificial Intelligence. According to data from OpenAlex, Mayetri Gupta has authored 24 papers receiving a total of 457 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 8 papers in Genetics and 5 papers in Artificial Intelligence. Recurrent topics in Mayetri Gupta's work include Gene expression and cancer classification (8 papers), Genomics and Chromatin Dynamics (8 papers) and Bayesian Methods and Mixture Models (5 papers). Mayetri Gupta is often cited by papers focused on Gene expression and cancer classification (8 papers), Genomics and Chromatin Dynamics (8 papers) and Bayesian Methods and Mixture Models (5 papers). Mayetri Gupta collaborates with scholars based in United States, United Kingdom and Australia. Mayetri Gupta's co-authors include Jun S. Liu, Joseph G. Ibrahim, Ivan Rusyn, Masami Asakawa, Masanori Matsuda, Hideki Fujii, Akira Maki, Tetsuya Suzuki, Hiroshi Kono and Jason D. Lieb and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Statistical Association and Blood.

In The Last Decade

Mayetri Gupta

20 papers receiving 442 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mayetri Gupta United States 11 277 60 59 57 51 24 457
Xi Long China 11 252 0.9× 46 0.8× 45 0.8× 9 0.2× 105 2.1× 31 445
Ryoichi Shimizu Japan 5 51 0.2× 16 0.3× 40 0.7× 34 0.6× 10 0.2× 38 240
П. С. Деменков Russia 16 433 1.6× 84 1.4× 49 0.8× 35 0.6× 64 1.3× 77 674
Yun‐Fu Hu United States 9 107 0.4× 102 1.7× 37 0.6× 7 0.1× 51 1.0× 11 358
Min A. Jhun United States 7 104 0.4× 150 2.5× 117 2.0× 4 0.1× 28 0.5× 10 334
Christian Tiemann Netherlands 11 429 1.5× 91 1.5× 50 0.8× 13 0.2× 19 0.4× 14 623
Yabing Huang China 9 124 0.4× 17 0.3× 24 0.4× 13 0.2× 19 0.4× 33 276
Matt Jacobsen United Kingdom 11 128 0.5× 11 0.2× 27 0.5× 23 0.4× 29 0.6× 23 303
Gift Nyamundanda United Kingdom 7 123 0.4× 12 0.2× 12 0.2× 19 0.3× 42 0.8× 16 278

Countries citing papers authored by Mayetri Gupta

Since Specialization
Citations

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

Fields of papers citing papers by Mayetri Gupta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mayetri Gupta

This figure shows the co-authorship network connecting the top 25 collaborators of Mayetri Gupta. A scholar is included among the top collaborators of Mayetri Gupta 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 Mayetri Gupta. Mayetri Gupta 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.
Nguyen, Hien D. & Mayetri Gupta. (2023). Finite sample inference for empirical Bayesian methods. Scandinavian Journal of Statistics. 50(4). 1616–1640.
2.
Swallow, Ben, et al.. (2022). Bayesian hierarchical mixture models for detecting non‐normal clusters applied to noisy genomic and environmental datasets. Australian & New Zealand Journal of Statistics. 64(2). 313–337. 1 indexed citations
3.
Nguyen, Hien D., et al.. (2020). Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions. Computational Statistics & Data Analysis. 152. 107040–107040. 2 indexed citations
4.
5.
Wu, Joseph, Mayetri Gupta, Amira I. Hussein, & Louis C. Gerstenfeld. (2020). Bayesian modeling of factorial time-course data with applications to a bone aging gene expression study. Journal of Applied Statistics. 48(10). 1730–1754. 4 indexed citations
6.
Moser, Carlee, Mayetri Gupta, Brett N. Archer, & Laura F. White. (2015). The Impact of Prior Information on Estimates of Disease Transmissibility Using Bayesian Tools. PLoS ONE. 10(3). e0118762–e0118762. 8 indexed citations
7.
Moser, Carlee & Mayetri Gupta. (2012). A Generalized Hidden Markov Model for Determining Sequence-based Predictors of Nucleosome Positioning. Statistical Applications in Genetics and Molecular Biology. 11(2).
8.
Hendricks, Audrey E., Josée Dupuis, Mayetri Gupta, Mark W. Logue, & Kathryn L. Lunetta. (2012). A Comparison of Gene Region Simulation Methods. PLoS ONE. 7(7). e40925–e40925. 3 indexed citations
9.
Gupta, Mayetri, Ching‐Lung Cheung, Yi‐Hsiang Hsu, et al.. (2011). Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations. Journal of Bone and Mineral Research. 26(6). 1261–1271. 42 indexed citations
10.
Long, Kimberly, et al.. (2010). The RNA Editor Gene ADAR1 is Induced in Myoblasts by Inflammatory Ligands and Buffers Stress Response. Clinical and Translational Science. 3(3). 73–80. 17 indexed citations
11.
Mitra, Rahul & Mayetri Gupta. (2010). A continuous-index Bayesian hidden Markov model for prediction of nucleosome positioning in genomic DNA. Biostatistics. 12(3). 462–477. 2 indexed citations
12.
Gelfond, Jonathan, Mayetri Gupta, & Joseph G. Ibrahim. (2009). A Bayesian Hidden Markov Model for Motif Discovery Through Joint Modeling of Genomic Sequence and ChIP‐Chip Data. Biometrics. 65(4). 1087–1095. 10 indexed citations
13.
Dworkis, Daniel A., Jacqueline N. Milton, Stephen W. Hartley, et al.. (2009). A Genome-Wide Association Study of the Alloimmunization Responder Phenotype in Sickle Cell Disease.. Blood. 114(22). 2551–2551. 1 indexed citations
14.
Gupta, Mayetri & Joseph G. Ibrahim. (2009). An Information Matrix Prior for Bayesian Analysis in Generalized Linear Models with High Dimensional Data.. PubMed. 19(4). 1641–1663. 20 indexed citations
15.
Walker, Nigel J., Deborah E. Burgin, Grace E. Kissling, et al.. (2008). Accumulation of M1dG DNA adducts after chronic exposure to PCBs, but not from acute exposure to polychlorinated aromatic hydrocarbons. Free Radical Biology and Medicine. 45(5). 585–591. 25 indexed citations
16.
Gupta, Mayetri & Joseph G. Ibrahim. (2007). Variable Selection in Regression Mixture Modeling for the Discovery of Gene Regulatory Networks. Journal of the American Statistical Association. 102(479). 867–880. 31 indexed citations
17.
18.
Gupta, Mayetri, Pingping Qu, & Joseph G. Ibrahim. (2006). A temporal hidden Markov regression model for the analysis of gene regulatory networks. Biostatistics. 8(4). 805–820. 11 indexed citations
19.
Maki, Akira, Hiroshi Kono, Mayetri Gupta, et al.. (2006). Predictive Power of Biomarkers of Oxidative Stress and Inflammation in Patients with Hepatitis C Virus-Associated Hepatocellular Carcinoma. Annals of Surgical Oncology. 14(3). 1182–1190. 111 indexed citations
20.
Giresi, Paul G., Mayetri Gupta, & Jason D. Lieb. (2006). Regulation of nucleosome stability as a mediator of chromatin function. Current Opinion in Genetics & Development. 16(2). 171–176. 17 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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