Susan Wei

1.5k total citations
31 papers, 1.2k citations indexed

About

Susan Wei is a scholar working on Molecular Biology, Artificial Intelligence and Genetics. According to data from OpenAlex, Susan Wei has authored 31 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 10 papers in Artificial Intelligence and 6 papers in Genetics. Recurrent topics in Susan Wei's work include Epigenetics and DNA Methylation (10 papers), RNA modifications and cancer (7 papers) and Cancer-related gene regulation (4 papers). Susan Wei is often cited by papers focused on Epigenetics and DNA Methylation (10 papers), RNA modifications and cancer (7 papers) and Cancer-related gene regulation (4 papers). Susan Wei collaborates with scholars based in United States, Australia and United Kingdom. Susan Wei's co-authors include Pearlly S. Yan, Huidong Shi, Farahnaz Rahmatpanah, Kenneth P. Nephew, Joseph C. Liu, Yu-Wei Leu, Robert Brown, Chuan‐Mu Chen, Tim Hui-Ming Huang and Ramana V. Davuluri and has published in prestigious journals such as Journal of the American Statistical Association, Cancer Research and Clinical Cancer Research.

In The Last Decade

Susan Wei

28 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Susan Wei United States 16 873 223 188 76 68 31 1.2k
Kaname Kojima Japan 17 453 0.5× 169 0.8× 95 0.5× 68 0.9× 50 0.7× 48 867
Sarah Hawley United States 19 542 0.6× 139 0.6× 328 1.7× 254 3.3× 52 0.8× 29 1.3k
Yi Zhu China 22 609 0.7× 234 1.0× 271 1.4× 125 1.6× 27 0.4× 75 1.2k
Trond Hellem Bø Norway 13 626 0.7× 86 0.4× 138 0.7× 123 1.6× 105 1.5× 19 991
Ingrid Lönnstedt Sweden 12 560 0.6× 176 0.8× 66 0.4× 40 0.5× 32 0.5× 17 792
Mahyar Sabripour United States 10 988 1.1× 164 0.7× 151 0.8× 53 0.7× 43 0.6× 12 1.3k
Natalie C. Twine United States 11 475 0.5× 108 0.5× 153 0.8× 194 2.6× 28 0.4× 15 881
Nigel Clegg United States 20 1.3k 1.5× 334 1.5× 406 2.2× 370 4.9× 25 0.4× 24 2.2k
Lei Song United States 14 466 0.5× 119 0.5× 288 1.5× 106 1.4× 31 0.5× 41 849
Geoff Macintyre Australia 15 671 0.8× 229 1.0× 313 1.7× 117 1.5× 22 0.3× 38 1.0k

Countries citing papers authored by Susan Wei

Since Specialization
Citations

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

Fields of papers citing papers by Susan Wei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Susan Wei

This figure shows the co-authorship network connecting the top 25 collaborators of Susan Wei. A scholar is included among the top collaborators of Susan Wei 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 Susan Wei. Susan Wei 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.
Wei, Susan, et al.. (2024). Variational Bayesian Neural Networks via Resolution of Singularities. Journal of Computational and Graphical Statistics. 33(4). 1464–1474.
2.
Li, Hui, Davis J. McCarthy, Heejung Shim, & Susan Wei. (2022). Trade-off between conservation of biological variation and batch effect removal in deep generative modeling for single-cell transcriptomics. BMC Bioinformatics. 23(1). 460–460. 7 indexed citations
3.
Wei, Susan, et al.. (2022). Deep Learning Is Singular, and That’s Good. IEEE Transactions on Neural Networks and Learning Systems. 34(12). 10473–10486. 3 indexed citations
4.
Wei, Susan & Marc Niethammer. (2021). The fairness‐accuracy Pareto front. Statistical Analysis and Data Mining The ASA Data Science Journal. 15(3). 287–302. 10 indexed citations
5.
Farris, Paul, et al.. (2021). Predicting Under- and Overperforming SKUs within the Distribution–Market Share Relationship. Journal of Retailing. 97(4). 697–714. 12 indexed citations
6.
Wei, Susan, et al.. (2020). Direction-Projection-Permutation for High-Dimensional Hypothesis Tests. UNC Libraries. 2 indexed citations
7.
Kosorok, Michael R. & Susan Wei. (2020). Latent Supervised Learning. UNC Libraries.
8.
Wei, Susan & Victor M. Panaretos. (2018). Empirical evolution equations. Electronic Journal of Statistics. 12(1). 1 indexed citations
9.
Larson, Nicole, et al.. (2017). A Low-Cost, Grab-and-Go Breakfast Intervention for Rural High School Students: Changes in School Breakfast Program Participation Among At-Risk Students in Minnesota. Journal of Nutrition Education and Behavior. 50(2). 125–132.e1. 23 indexed citations
10.
Wei, Susan & Michael R. Kosorok. (2013). Latent Supervised Learning. Journal of the American Statistical Association. 108(503). 957–970. 20 indexed citations
11.
Marron, J. S., Marc Niethammer, David Borland, et al.. (2012). Image and statistical analysis of melanocytic histology. Histopathology. 61(3). 436–444. 16 indexed citations
12.
Wei, Susan, Curtis Balch, Henry Paik, et al.. (2006). Prognostic DNA Methylation Biomarkers in Ovarian Cancer. Clinical Cancer Research. 12(9). 2788–2794. 118 indexed citations
13.
Chan, Michael W.Y., Susan Wei, Ping Wen, et al.. (2005). Hypermethylation of 18S and 28S Ribosomal DNAs Predicts Progression-Free Survival in Patients with Ovarian Cancer. Clinical Cancer Research. 11(20). 7376–7383. 57 indexed citations
14.
Leu, Yu-Wei, Pearlly S. Yan, Meiyun Fan, et al.. (2004). Loss of Estrogen Receptor Signaling Triggers Epigenetic Silencing of Downstream Targets in Breast Cancer. Cancer Research. 64(22). 8184–8192. 153 indexed citations
15.
Yan, Pearlly S., Susan Wei, & Tim Hui-Ming Huang. (2004). Methylation-Specific Oligonucleotide Microarray. Humana Press eBooks. 287. 251–260. 14 indexed citations
16.
Yan, Pearlly S., Susan Wei, & Tim Hui-Ming Huang. (2003). Differential Methylation Hybridization Using CpG Island Arrays. Humana Press eBooks. 200. 87–100. 32 indexed citations
17.
Wei, Susan, Robert Brown, & Tim Hui-Ming Huang. (2003). Aberrant DNA Methylation in Ovarian Cancer. Annals of the New York Academy of Sciences. 983(1). 243–250. 27 indexed citations
18.
Chen, Chuan‐Mu, Hsiao‐Ling Chen, Huidong Shi, et al.. (2003). Methylation Target Array for Rapid Analysis of CpG Island Hypermethylation in Multiple Tissue Genomes. American Journal Of Pathology. 163(1). 37–45. 80 indexed citations
19.
Yan, Pearlly S., Huidong Shi, Farahnaz Rahmatpanah, et al.. (2002). Applications of CpG Island Microarrays for High-Throughput Analysis of DNA Methylation. Journal of Nutrition. 132(8). 2430S–2434S. 67 indexed citations
20.
Carroll, Edward J., et al.. (1991). Purification, physicochemical characterization, and immunohistochemical localization of a major 11.7 S glycoprotein from the jelly coats of the anuran Lepidobatrachus laevis. Archives of Biochemistry and Biophysics. 284(2). 346–351. 9 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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